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The Sharpe Ratio is a well-known measure of portfolio performance. It is a ratio that allows for the comparison of various portfolios and allows for the measurement of their profitability. The mathematical definition is described as a division between the expected rate of return subtracted by the risk-free rate of return and the standard deviation of rates of return .
In practice, the ratio is calculated annually:
The ratio describes how much excess return you are receiving for the extra volatility that you endure for holding a riskier asset (). In simple words, the higher Sharpe Ratio is, the more portfolio is profitable.
Why is the Sharpe ratio so popular?
Most traders are still using the Sharpe Ratio because of its simplicity and ease of interpretation. It was proposed by Sharpe in 1966 and became credible to most users due to Nobel Prize in Economics for his works on the Capital Asset Pricing Model ().
Properties of the Sharpe Ratio
With reference to , the ratio has several properties. It is immune to manipulation by leverage. It can be interpreted as a T-statistics to test the hypothesis (see ) that the return on the portfolio is equal to the risk-free rate of return. The higher ratio is consistent with a higher probability that the portfolio return will exceed the risk-free return. The investor using the ratio has a utility (see ) whose only arguments are expectation and variance of returns.
Unfortunately, most traders forget that the Sharpe Ratio doesn’t apply to every data set. The central assumption of the ratio is that the distribution of the returns is normal. The financial market should be frictionless (without financial costs), and the risk-free rate of return should be constant and identical for lending and borrowing. Also, data used for computation should contain the initial capital of the portfolio.
Consequences of the ratio
Even though it is really hard to obtain the normality of returns on every possible data, the Sharpe Ratio has other disadvantages, which are represented in .
The ratio does not quantify the value added, and it’s only a ranking criterion. It has a hard interpretation when the value is negative. The Sharpe ratio does not make any distinction between upside risk and downside risk.
In the case of aggregation of portfolios, the correlation between volatilities is not included in the ratio. It is suitable for investors who invest in only one fund.
It doesn’t refer to a benchmark. The choice of a risk-free rate is rather important, though the impact is weak. The result highly depends on the initial capital of the portfolio.
The sampling error of standard deviation is embedded in the values of the ratio. By using the standard deviation of returns, the Sharpe measure puts both positive and negative variations from the average on the same level. But most investors are only afraid of negative variations.
As a consequence, the Sharpe Ratio leads to inappropriate results on particular sets of data.
When the ratio fails
To be aware of how important the assumptions are, consider the following example [*]:
When the expected return is negative, the Sharpe Ratio gives the inappropriate answer. The Sharpe Ratio from Instrument A should be greater than that from Instrument B.
|INVESTMENT A||SHARPE RATIO|
|INVESTMENT B||SHARPE RATIO|
The solution to Negative Returns
In the case of negative returns, the Israelsen modification of a Sharpe Ratio gives appropriate results. This ratio could be used as a verifying measure of a standard Sharpe Ratio (the values are meaningless, but the order remains accurate).
The Israelsen modification of the Sharpe Ratio from a previous example provides that Instrument A is greater than the Sharpe Ratio from Instrument B.
|INVESTMENT A||SHARPE RATIO ISRAELSEN|
|INVESTMENT B||SHARPE RATIO ISRAELSEN|
In conclusion, when negative returns occur, the investor should also check the value of the Israelsen modification before making the decision.
Avoiding invalid distribution
The most dangerous ratio usage is when the distribution of the returns isn’t normal. It seems that the best idea is to check the normality of the returns before use of the Sharpe Ratio. Several statistical tests can validate data distribution, such as Shapiro Wilk, Anderson Darling, Cramer von Misses, Dagostino Pearson, Jarque Berra, Kolmogorov Smirnov, Kolmogorov Lilliefors, Shapiro Francia (see ).
Generalizing Sharpe Ratio
On the other hand, there are different extensions of the Sharpe Ratio, which gives more accurate results by extending different assumptions. There are plenty of them described in , but only those with skewness and kurtosis will be presented.
Skewness describes the asymmetry of the probability distribution. If there are more extreme returns extending to the right tail of a distribution, it is said to be positively skewed, and if they are more returns extending to the left, it is said to be negatively skewed .
Kurtosis provides additional information about the shape of a return distribution. Formally it measures the weight of returns in the tails of the distribution relative to standard deviation but is more often associated with a measure of flatness or peakedness of the return distribution .
Skewness and Kurtosis are normalized forms of 3rd and 4th central moments, respectively (see ). Its combination with the Sharpe Ratio allows for approximating the ratio when the distribution is not normal. This method leads to the proposition from , where skewness divided by kurtosis is added to the Sharpe Ratio, which results in a new performance measure.
In the case when the owner of the portfolio has different preferences (different utility – see ), there are other ratios to measure the performance. The Adjusted for Skewness Sharpe Ratio allows measuring the performance using hyperbolic absolute risk aversion (HARA), constant relative risk aversion (CARA), or constant absolute risk aversion (CRRA). More details can be found in .
The Sharpe Ratio is a standard measure of portfolio performance. Due to its simplicity and ease of interpretation, it is one of the most popular indexes. Unfortunately, most users forget the assumptions and that results in an inappropriate outcome. They should consider checking the distribution of the returns or validating the results with other performance measures before making a decision on the market.
 Cogneau P., Hübner G., The 101 ways to measure portfolio performance, Université de Liège
 Bacon C., How Sharp is The Sharpe Ratio? – Risk-Adjusted Performance Measures, StatPro Group
 Zakamouline V., Koekebakker S., Portfolio Performance Evaluation with Generalized
Sharpe Ratios: Beyond the Mean and Variance, University of Agder, 2008
 Watanabe Y., Is Sharpe Ratio Still Effective?, Journal of Performance Measurement, 2006
[*] The original example is taken from the site:
From Crypto Signals to Profitability: the Path of a Crypto Investor.
How do you move from earning scraps off the crypto market to profitability? Like most crypto investors, you’ve had to or are probably still pursuing different paths to profitability. You’ve often asked yourself what it takes to succeed as a cryptocurrency investor. You’ve researched the different trade strategies, explored technical analyses, tried social trading, and even went after crypto signal providers. But none of these have what it takes to turn you into a profitable crypto investor.
In this guide, we detail the average crypto trader’s journey to profitability. We take you through the different stages most investors have to go through before they can eventually turn profitable. Almost always, it starts with manual trading crypto signals and social trading for crypto trading newbies before turning to automated systems. We explore this crypto evolution, detailing the different stages at which most crypto investors are currently stuck in before letting you in on the ultimate crypto investment strategy.
An investor’s crypto evolution is the journey they have taken in pursuit of profitability. It’s more of a learning curve with a common starting point where most crypto traders begin their investment journey. In most instances, traders start by interacting with such manual trading strategies as crypto signals. Others opt for the fully-automated crypto trading bots that are synonymous with the promise of massive returns.
Soon, they will shift to the more flexible DIY crypto trading bots that allow them to tweak the trade settings. But all these have serious faults that either cause traders to lose their investments or not make any tangible returns at all. This dims their ability to progress to pro investors. Let’s look at the stages of crypto evolution before looking at the ultimate crypto investor tool.
Crypto signals are some of the most popular crypto trading strategies for beginners. This involves linking up with a crypto signal provider, receiving trade signals, and entering into the guided positions. Different crypto signal providers, however, adopt varied tactics in their approach of the trade, with significant differences in, for example, the number of currencies they monitor, the mode of signal distribution, and signal fees. Let’s look at this in more detail:
Who Is a Signal Distributor?
Anyone with basic knowledge on how to interpret crypto charts can set up a crypto signal distribution service. A crypto signal provider is an individual or company that sends out crypto trading ideas and suggestions that its subscribers/followers can use to enter into trades. In most cases, these providers will claim to be crypto professionals or highly experienced traders. They will claim to have mastered the trade and are now just looking to help others, especially newbies, turn a coin trading the highly competitive crypto markets.
Crypto signal providers believe and have their followers believe that one can understand the markets by merely looking at the charts. They hold the opinion that they can understand the future of the markets by simply looking at its past operations. But they are wrong. And this line of thinking stems from the inherent human inclination to believe that we can draw patterns from even the most random data.
A signal service provider can also be a cryptanalysis algorithm programmed to identify the best trade entry points. When these points are triggered, the system sends out automated crypto trading signals to its subscribers.
Three types of providers, mainly the professionals that analyze the markets manually, algorithmic systems and a hybrid of algorithm and professionals traders dominate the crypto signal market. The professionals will send out signals based on the results of their market and chart analysis.
Automated traders, on the other hand, have automated systems that send out trade signals as soon as their preset trade entry triggers are hit. The hybrid signals providers, however, use the algorithms to analyze the market and identify trade entry points. But the automated trader’s results have to be counterchecked by a professional before being released to subscribers. They nevertheless have one thing in common; they all base their analysis on the results of a combination of technical analysis tools, latest news, rumors, and market conditions.
The trade signal comprises of three critical parts: the trade entry price, the take profit price, and the stop-loss price. Depending on whether you’re dealing with free signals or paid signals, the provider will advise you on the minimum trade amounts for breaking even and maximum profitability.
Some trade signal providers may also offer additional information such as analysis of the different trade prices like the entry price or stop-loss levels. But given the time-sensitive nature of these signals, the provider will only send out the trade entry points first and the information supporting these trade points much later. You, therefore, need to fully trust that the cryptocurrency signal provider has your interests at heart given that you will enter into virtually all crypto trades blindly.
Who Uses Crypto Signals?
Crypto signals are primarily used by crypto investment beginners that can’t yet skillfully analyze the crypto markets and accurately identify the best entry and exit points. Part-time crypto investors who don’t have enough time to properly analyze crypto markets and identify the trade entry or exit points have also mostly relied on crypto trade signal providers.
Depending on signal providers to help you get into trades, however, calls for a lot of trust on the part of traders. This means you need to properly vet the signal providers before subscribing to their services. You need to look at factors such as the experience and professionalism of the individuals behind the service.
You also need to look at their win-to-loss ratio and only subscribe to the provider with the highest win ratio. Additionally, you need to consider the frequency of their trades, their subscription fees, and minimum trade amounts as well as the mode through which they distribute these trading signals.
Modes of Signal Distribution:
There is a very short window of time between receiving the signal and entering into a trade. The speed at which you enter into a winning signal ultimately determines how much you make from the trade. As such, signal providers try to use efficient modes of communication in delivering these signals to as many of their subscribers as possible. Some of the most common modes of signal distribution include:
Texts: Text message is one of the most effective and most popular method of delivering the best crypto signals. The fact that virtually everyone has a phone makes it the perfect signal distribution tool. The text message alert is also hard to ignore.
Telegram bots: Telegram messaging comes in as yet another favorite for most signal distributors. It’s easy to see why it’s the most preferred communication tool. To begin with, it has an alert system similar to that of the text message that’s hard to ignore. Secondly, Telegram allows for the development of automated trading bots that are easy to integrate into the trading systems. These will automatically enter into a trade immediately the signal is received using preset trade parameters.
Emails: Emails also count as a form of signal distribution. However, most signal providers only have them as an alternative distribution option for the text or telegram messages. Sending signals via email isn’t popular and will only work for international clients.
But latency of these channels puts investors at a huge disadvantage in comparison to for example trading bots.
Charges and other fees
A cryptocurrency signal distribution channel can be either free or paid. INFOCRYPTO and Fat Pig Signals are two of the most popular and considerably accurate free signal service providers. These two are telegram based, meaning that most of their signals are delivered via telegram bots. There’s also a host of popular paid cryptocurrency trade signal providers that charge a monthly, quarterly or annual subscription.
Why you shouldn’t consider trading signals as a crypto investing strategy:
- Virtually anyone can open an online crypto signal distribution service and charge a subscription fee. This tells you that signals providers are out to make money – not by trading but from selling untested signals. Furthermore, if these signals were as profitable as they claim, they would open trades, not sell them.
- Most virtual signal providers indicate a disclaimer that their signals are just suggestions, hence no guarantee you will profit from their execution.
- You are obliged to pay the subscription fee whether you win, lose, or fail to trade all their trade suggestions. Most will also prefer upfront payment that compounds the loss incurred from trading fake signals.
- Most will either demand for minimum trade amounts or have their subscriber fees so high that you have to commit significant trade amounts if you wish to break even
- Latency in manually entering the trade may make the signal invalid or at best diminish potential profits
Social Trading Cryptocurrency
Social trading is one of the easiest approaches to investment. It works best for both new part-time traders and investors. To successfully invest in cryptocurrencies, you need a thorough understanding and experience in analyzing the markets. You also have to stay on top of recent news and events around the crypto industry. And lastly, you have to be adept enough to predict the impact of current trends on the price of digital currencies. Social trading and the different social trade platforms that actively promote and allow copy trading can enable you to bypass the need for industry experience.
How does social trading work?
Social trading is a form of networking in crypto trading that involves sharing trade ideas and signals. The most popular form of social trading today is copy trading. Copy-trading allows a trader to copy the trade strategies of another more experienced trader. Interestingly, social trading can be implemented at both the trader expert levels exchange and trading platform level. eToro, ZuluTrade, and NagaTrader are some of the crypto trading platforms that lead the pack in supporting copy trading.
Usually, such providers will have a list of expert traders on their platform that allows others to copy their trades. Highlighted on their profiles will be such factors as their win ratio, trading frequency, coins invested in, risk tolerance levels, and historical trade accuracy. The platform will then charge either a subscriber fee or a commission on earned profits. For instance, eToro charges copy traders 20% of profits made from copying trades.
Like crypto signals, copy trading ensures you don’t need crypto trading experience to invest in cryptocurrencies. You only need a platform that lets you copy trade while maintaining the lowest social trading fees and commissions. You also need to master the art of identifying the most viable expert trader to copy from, paying particular attention to their win ratios and risk tolerance levels.
Why social trading is not the best approach to crypto investing:
- There is no guarantee that the expert trader you are copying will continue with their winning streak or good performance
- You will not always find an expert trader you are compatible with, especially when it comes to risk ratio
- Some platforms charge exorbitant fees and commissions that negatively influence your results in the long run
- Fee structures can push expert traders on the platform to risk-seeking trades thus wiping out trader accounts if the markets don’t go their way
- Social trading doesn’t create enough room for investment portfolio diversification, which additionally puts large risks on investors
BlackBox Crypto Bots
The internet and the cryptocurrency trading space isn’t short of BlackBox crypto trading bots. These are tools that claim to automate the entire crypto trading process and help you scoop unprecedented profitability, while all you have to do is buy the bot or subscribe to their service. According to their vendors, you don’t need to be a professional or highly experienced crypto trader to profit from crypto trading as these pieces of software have the work cut out for you.
The bots and software are fully automated and require little to no effort on the trader’s part. They also have efficiency, speed, and accuracy as their biggest selling points as their vendors boast of their ability to outsmart an average trader both in trade analysis and execution speeds. But even with these seemingly elite features, they have serious faults that make them unfit as a crypto investment tool.
How Crypto Bots Work
Ideally, a crypto bot is an automated trading software that crawls the cryptocurrency market looking for the best trading opportunities. Crypto bots usually follow a specific set of trade instructions when analyzing the market and identifying the best trade opportunities. These instructions are based on factors such as technical indicators, trends or price levels.
With most of the black box crypto trading bots, the settings are designed and updated by the developers of the automated trader. They leave little to no room for customization. Most are online-based as this makes it easy for developers to tweak or overhaul the trade settings more conveniently. However, when dealing with offline trading bots, the provider will send you a link where you can download new or updated trade settings and manually load them on to the bot.
Most of these bots have also embraced different levels of specialization and most crypto bots available in the market today are either coin specific or exchange specific.
Coin specific bots
These are crypto bots that are centered on the performance of a single coin. The most popular is the bitcoin bots that track and monitor bitcoin activities. Their trade settings are centered on the legacy cryptocurrency and can, therefore, only be used to trade bitcoin and bitcoin cash. They are referred to as black-box bots because there is little you can do to tweak their trade settings and their logic is generally hidden from your eyes.
Exchange specific bots
These are crypto bots developed and distributed for one crypto exchanges and its API. The bots are developed by independent crypto analysts and traders but still center on the operations of a specific exchange. Exchange specific bots will only trade some or all the coins listed on the exchange. Note also that most of the exchange specific bots often align themselves with some of the most popular exchanges like Binance, Coinbase or Gemini. They are especially popular with crypto exchanges that support leveraged crypto trades like BitMex, Kraken, and Deribit.
Who can use the bots?
Inexperienced traders: Most of these black box crypto trading bots are marketed as the ultimate hands-free and wholly passive approach to trading. And this appeals most to inexperienced crypto traders looking for a quick way to make money off the crypto market. The automated crypto trading tool especially comes in handy in allowing the inexperienced crypto enthusiast to scrap small profits from the market as they learn how to trade.
Part-time traders: Part-time crypto trading isn’t the best approach to crypto investing. The crypto market is highly volatile and leaving trades open for long – with insufficient risk management protocols in place – can expose a part-time trader to unimaginable losses in case of unexpected market downturns. Trading part-time also means that you have less time to effectively analyze the markets and set up informed trades. For this reason, most of the part-time traders have, therefore, turned to automated crypto trading bots. Their ability to use preset trade settings and incorporation of sufficient risk management tools ensures that they only enter trades when conditions are ideal.
Why crypto bots aren’t the best approach to crypto investing:
- Crypto bots are highly complicated software and arriving at a quality bot involves a lot of trial-and-error and loss of valuable capital.
- There is a lot of secrecy surrounding these automated crypto trading bots. Hardly will you ever know their developers are and this makes it hard to trust a system with your cash when you don’t know the professionalism or ethics of its developers.
- Most crypto trading bots are cloud-based and will automatically receive and execute trade signals or trade settings. They claim to use technical analysis to identify trade opportunities but will never reveal some of the indicators or trade strategies that they use.
- Fraudsters and privacy compromisers have continually targeted and infiltrated the automated crypto trading niche. Also, we still don’t have a proven method of vetting the quality of bots, the experience of their developers, the effectiveness of their strategy, and how they handle private data.
- Some automated crypto trading bots have proven time and again to be a scam only interested in your subscription fees and private data like exchange keys, and vanishing as soon as they have both. You will end up with not only your privacy compromised, but also lose both the bot subscription fee and the crypto trading capital you had invested.
- No opportunity to learn: Since everything is automated, plus the bot doesn’t share the trading strategies and technical analysis indicators used to arrive at the trades, you never get to learn how to trade.
DIY Crypto Trading Bots:
After trying crypto signals and facing the disappointment that is black box crypto trading bots, the crypto investor’s evolution journey pushes them to the DIY crypto trading bot. These DIY trading bots are automated cryptocurrency trading tools. Unlike the black box auto trading tools, however, the DIY auto traders are more professional and more transparent about their system designs and trading strategies.
They also claim to rely on technical analysis and elaborate trade strategies to analyze the market. Their DIY nature also sets them apart from the wholly automated black box auto traders that do not leave room for the customization of trade strategies. With crypto trading bots, you can tweak the default trade settings to align with factors like your risk tolerance.
Their highly transparent nature also makes it easier for you to better understand how the crypto market works. For instance, you can tell the strategy used by your DIY trading bot which technical analysis indicators should be used in monitoring the market. You can also vet its trade entry and exit decisions by setting the factors the bot takes into account before opening a trade position, risk management protocols observed for each trade, and factors triggering a sell-out and exit.
These are all part of learning that help you become a better crypto trader. In essence, the DIY bots address the inherent limitations of relying on crypto signals and the black box crypto trading bots in kick-starting your crypto journey. Two of the most popular DIY crypto trading bots in the market are CryptoHopper and 3Commas. Let’s take look at each of these below:
How Cryptohopper Works:
Cryptohopper is a Ruud Feltkamp’s project that’s currently available to crypto traders in both automated and semi-automated versions. The bot uses market and exchange arbitrage to make profits. The Cryptohopper app is also a strategy design tool that allows the user to customize trading strategies.
Cryptohopper uses technical analysis indicators such as RSI, EMA and Parabolic Sar to scan the markets. Its semi-automatic version is more of a social trading tool that copies the trade strategies of the most successful day traders and sends them out to the rest of the community.
The semi-trading bots scour the market for the best performing trade strategies, copy them, and present them to the trader. It sources trade signals from different traders and forwards them to the trader. The bot essentially presents the trader with a choice and they get to only implement the signals that they feel best matches with their trading strategy.
How 3Commas works:
3Commas describes itself as a smart trading terminal and a combination of trading bots. It features both auto-trading and manual trading systems and uses technical analyses to monitor the charts and markets around the clock. Some of the technical indicators used on the platform include CQS scalping and RSI. Plus it also supports social networking and copy-trading.
Limitations of the DIY crypto bots as professional investment tools:
While the qualities and trading features of the DIY crypto bots dwarf the crypto signals and black-box crypto bots, they are still a far cry from helping traders reap maximally from the crypto market.
- They are based on the idea that finding the right combination of simple technical analysis indicators can bring profits. Maybe it could, but this needs very good backtesting capabilities with large datasets of historical data, which all of these platforms lack.
- With current states of computing, finance, and mathematics, there are more advanced tools available than moving averages or other technical indicators capable of identifying patterns on the market
- These platforms lack the technical performance needed to react to market events. A latency of a minute or seconds outperforms signals or social trading, but is not enough to win the algorithmic race. On the contrary – you are an easy target for more sophisticated algorithms.
- Most DIY crypto trading bots have highly complicated set up processes and getting it wrong at this stage sets you up for losses every time you enter a trade
- Dealing with the bot signals and their different trade settings like where to place the stop loss and take profit levels can be quite confusing
- Most DIY bots are premium service providers that charge subscription fees. Mostly, the profits you make off your returns and time spent configuring trade settings aren’t always commensurate with the subscription fees. They have a high expense to income ratio which makes them unideal trade platforms
- Most of the DIY cryptocurrency trading tools pay more attention to social trading than educating the subscriber on how to improve their trading skills
- Most of the bots are poorly maintained and thus prone to system lags that in turn result in expensive slippages.
Ultimate Crypto Investment Tool: Professional Trading Bots
After looking at the different stages in a crypto trader’s evolution journey towards profitability and why they don’t work, you must now start revaluating your trading style. And looking at some of the reasons why it doesn’t work, you can tell that there is little you can do to turn these trading strategies around. In such a case, what are your options? Who do you turn to, especially if you are looking to undertake significant crypto investments? Well, you can always turn to professional trading bots used by expert traders and institutions.
How Do Professional Trading Bots Work?
These too are automated cryptocurrency trading systems and work to achieve pretty much the same goal as the DIY and black box crypto trading bots. However, they differ from the rest of the automated trading bots in profitability. Unlike the crypto signals and the rest of the automated tools provided by faceless individuals and companies with questionable crypto trading experience, professional bots are designed and tested by pioneer crypto traders and some of the most experienced crypto and money market trading professionals.
You will nonetheless appreciate the fact that most of these bots and platforms are proprietary and expensive. The high price is in appreciation of the time and effort as well as the resources utilized to come up with such a bot and underlying a platform that can react to market events in milliseconds, processing information on hundreds of instruments in real-time. In most instances, these bots often take months or years of hard work from highly qualified teams of professional quants and crypto traders.
Who Uses Professional Trading Bots?
Developing sophisticated and profitable systems requires huge time and capital investments. You need enough money to hire quants to create, test, and roll out these algorithms.
Hedge funds are the primary inventors and beneficiaries of these professional crypto trading bots. They have funded the development of and own more proprietary algorithms than any other entity. And they present you with the best chance of having the professionally developed crypto bot work for you. Most don’t discriminate and will welcome any client on board as long as you can raise their minimum initial deposit.
Institutional investors like mutual funds and private equity funds may also come together to fund the development of proprietary cryptocurrency algorithms.
What makes professional trading bots superior to all other approaches to crypto investing?
Here are the unique factors that make professional trading bots better than the conventional DIY and BlackBox auto traders, as well as reasons why you too should consider using one.
- Execution strategies: Virtually all professional trading bots use a combination of different trade execution strategies. And they are all aimed at helping the bot achieve maximum profitability with every executed order. The most common include:
- Smart order routing: Professional cryptocurrency bots aren’t crypto or exchange specific, but are specially designed to monitor the entire industry for the best prices. The algorithm is constantly scouring the markets looking for an exchange with the most promising prices for your orders and executing trades automatically.
- Advanced order types: A professional crypto trading algorithm also uses advanced order types not available to the retail auto traders. These include the iceberg order that hides the real size and value of your orders to ensure the market doesn’t go against you. Alternatively, it could employ the pegged order system where the algotrader passively follows the highest list of trades in the order book in a bid to achieve the best execution prices.
- TWAP and VWAP strategies – are execution algorithms that help you make large trades without influencing the prices
- Passive strategies: The bot will have liquidity provision strategies like market-making. These involve hedging and exposing your capital to minimal risk while benefiting from differences in spreads.
III. Arbitrage strategies: Arbitrage involves buying one cryptocurrency on one exchange and selling it on the other to take advantage of the price differences. It can either be standard arbitrage that involves just one cryptocoin like Bitcoin or Ethereum, or triangular arbitrage for more advanced bots that take advantage of the price difference of up to three coins in different exchanges. Other arbitrages will go as far as specializing in statistical arbitrage and cross-trading a portfolio of crypto assets simultaneously in different exchanges.
- User-defined strategies: Some bots will also have a host of advanced and statistics-based execution strategies that capture and process the widest range of data in the shortest periods to provide the trader with multiple possible correlations that can be exploited to create maximum profitability opportunities.
- Connect to both multi-list and niche exchanges: Bots for long running of stable strategies need to have regularly updated connectors to all the major crypto exchanges like Binance and Coinbase. It should also have connectors to the not-so-popular niche exchanges and over-the-counter crypto platforms. More important than the long list of connected crypto exchanges is the quality of these connectors. Exchanges are constantly changing their APIs and a professional firm who monitors and updates its connectors fast can defend you from losses that would arise from connectivity issues.
- Managed and updated by professionals: Unlike the basic crypto algorithms from faceless providers, the developers of different professional algorithmic traders are industry leaders. They are charged with the day to day management of the system by keeping it and your invested capital safe, updating its core features regularly and ensuring that it maintains optimal performance.
How do you access the professional trading bots?
We already mentioned that these professional bots have for the longest time been a preserve for the capital and digital asset management firms like hedge funds and investment banks. They are proprietary property and the tools behind the success and consistent profitability posted by these firms. Traditionally, you would have to first research the different hedge funds, looking at what they say about the features of their bots before raising a high minimum initial deposit to become one of their clients.
The recent past has however experienced a gradual change in the finance and investment industry with most institutions moving away from the traditional and rather rigid modes of business. And with this industry shift, most of these professional bots have been availed to the retail market at affordable terms for the average investor. Therefore, you no longer have a reason to continue clinging on to the frail crypto signals, the deceitful black box auto traders, the faceless ‘expert’ developers or the expensive and unprofitable DIY cryptocurrency trading bots. Learn how to access the professional proven and tested professional trading both here and start recording hedge-fund-like profits.
It’s known that almost all industries are influenced or about to be influenced by the appliance of Artificial Intelligence. Perhaps operational efficiency is what makes Artificial Intelligence so attractive for business owners across different sectors. Operational efficiency could lead to the reduction of costs, increased performance, speed up some processes or increase the quality of services.
In this article, we would like to cover the appliance of Machine Learning across the financial industry by presenting interesting use cases and examples to structure this content.
Artificial Intelligence is assisting financial institutions to drive new efficiencies and deliver new kinds of value. Autonomous Research predicts that Artificial Intelligence will represent $1 trillion in projected cost savings for the banking and financial services industry. By 2030, traditional financial institutions will save 22% of costs.
Let’s get something straight here and let’s define Machine Learning vs Artificial Intelligence. These two terms are always used side by side of each other, but they are different. With different, we mean that Machine Learning is a subset of Artificial Intelligence. Artificial Intelligence refers to create intelligent machines. Machine Learning refers to a system that can learn from experience. In this article, we may mention both but with the given simplistic definition you already know what we are referring to.
Challenges of applying Artificial Intelligence in finance
Before jumping into the use cases that we have gathered for this article, let’s take a look at the most common challenges of applying Artificial Intelligence in firms within the financial industry.
As previously mentioned costs and budgeting required to automate some of the processes in finance could be one of the main important challenges financial firms face. Additionally, regulatory requirements sometimes could be also a burden. They are complex frameworks and the required research phase could be time-consuming and hiring regulatory consultants could be costly.
Perhaps another challenge these firms face is the lack of structured or sufficient data to process and train their data and test if models are efficient enough. Adding to that lack of in-house skills and knowledge as well as missing the development environment (lab) that data scientists can join and apply approaches of AI.
Another challenge could also come from market maturity and retail readiness to use Artificial Intelligence-powered tools.
Case 1. Fraud detection
One of the very important appliances of Machine Learning in finance is fraud detection. With the advent of instant payment and global transfer services, the volume of payments and transfers has dramatically increased. So is a notable amount of transfers that aren’t with good intentions including money laundering. The estimated amount of money laundered globally in one year is 2-5% of global GDP, or $800 billion to $2trillion.
An advantage that Machine Learning has brought to fraud detection is the amount of data that can be processed by machines with minimum or zero human intervention. The appliance comes with more accuracy in the detection of fraudulent activities.
For instance, the credit card fraud detection problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be a fraud. This model is then used to identify whether a new transaction is fraudulent or not. The aim here is to detect fraudulent transactions while minimizing incorrect fraud classifications. Anomaly detection is a commonly used model for the credit card fraud detection problem, this is a technique used to identify unusual patterns that do not conform to expected behavior, called outliers. Anomaly detection or pattern recognition algorithms start with creating processes that find the hidden correlation between each user behavior and classify the likelihood of fraudulent activity.
Discovering hidden and indirect correlations can be named as advantages that Machine Learning based algorithms bring compare to basic Rule-based fraud detection algorithms. Machine Learning-based algorithms also reduce the number of verification measures since the intelligent algorithms fit with the behavior analytics of users. A more automated approach to detection of fraud is also another asset for Fraud Detection algorithms applied by Machine Learning since they require less manual work to enumerate all possible detection rules.
Case 2. Know Your Customer (KYC)
Improving the KYC process is one of the operational efficiency of artificial intelligence and machine learning algorithms that have been brought to the financial and banking industry. The appliance of Machine Learning on the KYC process is mostly implemented by traditional banks and neobanks. The main reason is the continuous evolvement of requirements from regulators. The due diligence required on customer registration that is required by regulators in banking is broad and complex. Machine Learning due diligence modules can be utilized to create robust automation and improve the process of KYC for institutions that are aiming to have an efficient retail onboarding. This will decrease the human intervention needed during the onboarding process and increases the accuracy as well as reducing the costs.
Again neobanks are streamlining the KYC process with enhanced user interface and user experience. Simplifying and automating the KYC process can reduce the cost of onboarding and customer application process by 40% (source Thomson Reuters).
One of Machine learning techniques used in the KYC process is the Facial Similarity check, which is to verify that the face in the picture is the same with that on the submitted document provided e.g. Identity Card. The customer will only be verified and pass the KYC process if the results of both Document and Facial Similarity checks are ‘clear’. If the result of any check is not ‘clear’, the customer has to submit all the photos again.
Case 3. Algorithmic Trading
The algorithmic trading with a technological infrastructure brought many advantages to the trading world e.g. the ability to trade in under a millisecond with the best prices available or the ability to simultaneously monitor and trade across multiple exchanges, and all with reducing the human error from trading. Algorithmic trading constitutes 50-70% of the equity market trades and 60% of futures trades in developed markets.
Many hedge funds started to utilize Artificial Intelligence within the algorithmic trading world. It’s understandable that most of them do not disclose the details and mechanism of their approaches in applying Artificial Intelligence in their trading algorithms, but it’s understood that they use methods of Machine Learning and Deep Learning. There is also a wide appliance of sentiment analysis on the market in which the result can be used in trading. The main objective of applying sentiment algorithms is to obtain knowledge about the psychology of the market.
Machine Learning is assisting the trading industry in order to leverage the market with fundamental and alternative data in order to research alpha factors. Supervised, unsupervised and reinforcement learning models are being utilized to enhance the processing of algorithmic trading strategies. Methods can be applied to optimize portfolio risk calculations and further improve the performance of the portfolios.
Deep Learning models also have been widely applied in trading. Deep learning models with multiple layers have shown as a promising architecture that can be more suitable for predicting financial time series data. In a tested practice, the algorithm trains 5-layer Deep Learning Network on high-frequency data of Apple’s stock price, and their trading strategy based on the Deep Learning produces 81% successful trade and a 66% of directional accuracy.
Case 4. Chatbots and customer support
Reducing customer churn is perhaps one of the main criteria of financial institutions and banks. Generally, customers and especially millennials ones for neobanks do care about the customer service and support they receive. Chatbots and instant messaging apps could potentially increase communication quality between business and its customers. According to research by Juniper by the year 2023, the use of chatbots can reduce the operational costs for banking, retail, and healthcare business sector by $11 billion.
The advantages that chatbots can bring to the industry are definitely increasing customer satisfaction and customer engagement rates. The speed of action and processing of many inquiries and threads at the same time could also be mentioned as a big advantage of chatbots and messaging systems.
There are four types that Chatbots could be classified. Goal-based Chatbots, are designed for a particular task and set up to have a short conversation in order to complete a task given to them by a user. Perhaps this is the most common Chatbot. Goal-based Chatbots are deployed on websites to help visitors to answer their questions during their visit.
The second type is knowledge-based Chatbots, these Chatbots use the underlying data sources or the amount of data they are trained on. Such data sources could be open-domain or cloud domain data. Usually, Knowledge-based Chatbots answer questions by providing the data and source of that data.
The third category of Chatbots is Serviced-based. Such Chatbots are classified based on facilities provided to the customer. It could be personal or commercial information. Users of such Chatbots could place an order of a commercial good via the Chatbot.
The fourth category is Response-generated Chatbots. Response Generated-based chatbots are developed based on what action they perform in response generation. The response models take input and output in natural language text. The dialogue manager is responsible for combining response models together. To generate a response, the dialogue manager follows three steps.
Case 5. Automated Wealth Management with Robo Advisors
Wealth management is an industry and operation costs are a large burden for some of the firms. As wealth being transferred from one generation to a more tech-savvy one and considering the millennials are in their prime earning and spending years, the presence of automated and entirely digital investment advice tools can be expected. It’s expected that by the year 2022 the Robo Advisors revenue might reach 25 billion that is up from $1.7 billion from 2017, considering these tools are relatively cheaper to how the investment advice is being delivered traditionally, from 3% to 5% of assets managed to digital ones with 0.25% to 0.75%.
There are practiced used cases where the processes of asset allocation modeling, portfolio construction, and optimization, as well as a portfolio recommendation systems, were bundled with Machine Learning techniques in order to enhance the current approaches.
One example is the portfolio recommendation system that was designed to be implemented on top of a Robo Advisor and be utilized with the mean-variance optimization method was implemented using weighted linear regression. The model shows that adding a portfolio layer on top of the stock regression results is increasing the success rate (profit accuracy) up to 86.69% when success is calculated by the profitability of the recommendations. Moreover, it helps to reduce the risk by distributing the budget over a set of stocks and tries to minimize the reflection of the regression errors to the profit.
Robo Advisors come with many notable advantages, such a complete, online and real-time reporting dashboard to customers which can be checked on the go with mobile apps and dashboards.
As previously mentioned they reduce the costs of operations for firms providing investment advice service and the fees that are clients charged. Robo Advisors are fully digital and they have online onboarding for clients which leads to expansion of client base for firms.
When can you apply AI (is your firm ready?)
There are few aspects to which we could measure the readiness of a firm to utilize Artificial Intelligence and Machine learning into their processes. A solid technological infrastructure is the most important element. An infrastructure that is put together to manage the whole lifecycle of data, from getting to cleaning to processing and feeding algorithms. The availability of the data can not be stressed more.
The regulatory compliance as mentioned in some of the cases above e.g. in KYC processes is a crucial process to be taken care of before applying Artificial Intelligence into processes. Audit trails, transparency, result supervision, and reporting mechanisms are some of the high-level requirements from financial authorities.
Talents as resources from data engineers to data scientists specialized and familiar with financial processes is another important criteria before kick-starting with Artificial Intelligence projects. Their ability to understand the sector and ways to improve it should be taken into account in their hiring process. Eventually, they need to start training the existing data with an accuracy level as a requirement for the models used.
How to start your machine learning project?
- Start with a question
Before anything starts you need to start with the question, what is it that we want to improve with our Machine Learning algorithms? This should specify and clarify the objective of the project.
- Understand your data
Not every question can be answered with any data. You need to have the right data for the right question. This is practiced by receiving, cleaning and processing data. Running exploratory analysis on your data and making sense of some of the summaries obtained could be the initial stage to which you will know that if your data has the potential to answer your questions.
Once you found clues in your data associated with your question it’s time to try to write algorithms to find patterns that leads to successful or unsuccessful journeys. Usually, data scientists do this by fitting the most suitable Machine Learning models into the data, find correlative and statistically significant patterns and try to test the accuracy.
In the modeling section, we talked about training your data and once we have found the best models that suit the question, the answer and the data and now its time to evaluate, in other words, test your models. Data scientists will keep on testing the models with new data to see if their models do not only work for one dataset.
Once the fitting algorithms are certain that works, it’s time to deploy. Generally, this means deploying a code representation of the model into an operating system to score or categorize new unseen data as it arises and to create a mechanism for the use of that new information in the solution of the original business problem. Importantly, the code representation must also include all the data preparation steps leading up to modeling so that the model will treat new raw data in the same manner as during model development.
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With the prospering cryptocurrency market and lots of brilliant Blockchain ideas that have been born in recent times, not all of these projects and their coins survive the market. One indicating factor for this outcome is coins being delisted from exchanges that accepted to list them. Although different exchanges have different delisting policies and reasons for delisting coins from their exchange we have found a set of common patterns amongst them.
At a high level, we can categorize the delisting into two categories and then we break one down. Why one only, because the other one is really a dead project. Meaning, the team behind the project (the coin) has been dismantled and the exchange team has found out that this is no longer the case for investment. How do they know that? well, they research, they try to make contacts with the team behind the listed coin, they face unresponsiveness or not sufficient evidence that makes them believe that this project behind the coin is relevant anymore, these are labelled as failed projects. No one can do anything about this.
On the other hand, there are coins and projects that are genuinely alive, there are teams that are working around the clock to keep those coins and projects alive and they don’t want to be delisted, we plan to break this case down for you as the reasons for delisting vary.
Regulatory requirements and compliance. After all, being listed, being tradable is considered as a financial operation. Regulators are keeping track of coins that are being listed and evaluate their legitimacy. Unlicensed securities and financial products are subject to delisting from exchanges. Regulatory requirements could also evolve and coins need to be reactive to newly updated frameworks otherwise delisting will await them.
Another notable issue that causes delisting is a poor technical implementation which could lead to more fees for the exchange to maintain the coin listed. In most cases, the blockchain or related technology becomes compromised or defective.
Adding to the technical issues, a lot the coins and tokens traded between 2017 and 2018 were created as the ICO’s using existing blockchain’s programmable infrastructure and using specific token standards such as ERC-20. This means that most of ICO’s tokens were quite similar. A lot of these coins were listed on exchanges were simple as all these tokens are supported by the same protocol and wallets, and no improvements were made. This has caused a lot of delisting of coins across exchanges as well.
The security vulnerability could also be categorized as technical issues with coins that are being delisted. This type of delisting is due to technical maintenance costs for the exchange. Some of the technical obstacles with coins reach a point at which the developers of the exchange become aware of security alerts or technical ambiguity in the coin’s network that put the exchange, as a large holder of the coins, in a potential threat of loss in values or holdings.
Another important reason was coins were delisted, was wash trading or insufficient liquidity. Major Exchanges may delist a coin if it attracts a small number of traders, even if they are generating large volumes and trading fees due to wash trading, as they believe building a strong customer base is very important while artificial volume has little to no impact on the long term success of the asset while it hurts the reputation of the exchange. This delisting policy keeps wash traders away from the exchanges.
On the other hand, consistent liquidity is also a key factor for delisting. While most of the crypto traders are focused on top coins, some do trade smaller coins as well. Some due to initial holding of those from the ICOs and some small portion of interest on the project. Either way, exchanges tend to delist those coins that are not drawing enough traders to them since the cost and maintenance of those coins for the exchange are not enough in order to make profits from trading fees that they charge from traders. Many liquidity providers tend to work with these coins in order to provide enough liquidity to keep the coins listed in the exchange and also draw traders to and maintain their value.
Many coins and projects that have tokenized and are listed on exchanges do face this issue. After all, they need liquidity and volume in order to stay on the exchange and do attract new traders to trade and include their coins into their portfolios.
Some would choose the inorganic, fake and fast way of injecting liquidity into their coins. You guessed it right, through wash trading. Even though wash trading arguable could be used as fast or even maybe the cheaper approach to this, it may not be the best long term solution. One, because professional traders do and can spot wash trading activities on order books, two, many exchanges do not tolerate wash trading and do delist those tokens once they detect suspicious activities around those coins. For who would like to stay on track for a foreseeable future the best approach is to provide real liquidity to their coins.
Many Market Makers and liquidity providers focus on providing real liquidity to coins that either newly listed on exchanges or are lacking enough liquidity. There is a variety of off the shelf solutions available in the market. These solutions are known as Market Making Bots. Some of these bots are using as profit-making bots which do so with the typical approach to market making which is placing bids and asks on both sides of the market, be present in the market and compete and at last, make some profits from the difference in the spread and market movements.
Market Making bots are a good solution but perhaps not the best for a more sophisticated approach to market making. This is mainly because of limited parametrization available bots in the market. Another notable disadvantage of market making bots is that they are not built to manage and deal market fluctuations well when they occur, this is especially when the prices in the market are going down.
So what would be the best solution? Perhaps, more sophisticated market making algorithms and software that are configurable to different market situations. This is important especially because of risk management.
So what makes a good market maker when it comes to market that lack liquidity and in the danger of getting delisted, the following points are the most crucial in the liquidity provision journey:
- First and foremost, always-be-present-in-the-market. Constantly quoting in the market is an important criteria for improving efficiency and price discovery in an illiquid market.
- Reasonable quoting strategy. Quotes cannot exceed a specified percentage away from the best bid and offer
- Control the spread. Narrower spreads will induce both informed and uninformed traders to trade which in turn increases price efficiency and quickens price discovery.
- As an additional option, some sophisticated tools may be able to mirror instruments from other exchanges (usually more liquid exchanges) to get a good understanding of the market changes. This can be an issue because of volatility reasons, but some tools have a way around that.
Deribit Trading bot
Introducing Deribit Cryptocurrency Exchange:
Deribit is a cryptocurrency derivatives exchange allows for Ethereum and Bitcoin trading on Options, Perpetual and futures trading. Deribit keeps its customers’ deposits in their cold storage with most of the funds stored in vaults with multiple banks safes. Though at this point in time, Deribit only accepts Bitcoin and no fiat currency as funds to deposit. Deribit allows fully automated trading through its API.
For futures trading on Deribit, traders receive a cash settlement instead of physical Bitcoin trading, that means the buyer of the futures do not buy the actual Bitcoin and the seller won’t sell the actual Bitcoin and what will be transferred is the transfer of profits and losses at the agreed settlement price of the contract on the expiration price.
The minimum tick size to trade futures is 0.50 USD and all daily settlements happen at the coordinated universal time (UTC) 8 am. The expiration time is also at UTC 8 am at the end of each month. The size for the contracts at Deribit future exchange is 10 USD with initial margin starting at 1.0% with a linear increment of 0.5% per 100 BTC. The delivery price is the Time Weighted Average of Deribit Bitcoin index measured in the half an hour before the UTC 8 am (7:30 am).
At Deribit exchange, Perpetual is a derivative very similar to Futures trading but with no fixed maturity and no exercise limit. The perpetual derivatives are to keep their price close to their underlying cryptocurrency price, which at Deribit exchange is referred to as “Deribit BTC Index”.
The Deribit Perpetual contract is 1 USD per Index Point with a contract size of 10 USD. The minimum tick size 0.50 USD and settlements are done at UTC 8 am. The contract size for trading Prepetuals in Deribit is 10 USD. As mentioned earlier there is no delivery/expiration when trading Prepetuals on Deribit.
Options on Deribit are traded with what so-called the “European style”. This indicates that options cannot be exercised before expiration, but can only be exercised at expiration. Which this happens automatically on Deribit. Options on Deribit are priced in Bitcoin and Ethereum and also viewable on USD.
There are 8 exchanges and the highest and lowest prices are taken out, and the remaining 6 are each at 16.67% accountable for creating an index in Deribit.
Market Making on Deribit:
Deribit does not include an in-house trading desk, therefore all active market makers are the third party Market Makers. The liquidity is provided by these parties and Deribit sees these services as a crucial point to their business. Based on the volume, designated market makers receive tailored agreement on fees.
For automated trading software and trading bots, Deribit provides three forms of integrating to its API, the FIX (financial information eXchange) API, JSON-RPC over Websockets API and JSON-RPC over HTTP.
Deribit utilizes JSON-RPC which is a light-weight remote procedure call protocol. The JSON-RPC specification defines the data structures that are used for the messages that are exchanged between the client software and the server, as well as the rules around their processing.
JSON-RPC is transport agnostic, it doesn’t specify which transport mechanism must be used. The Deribit API supports both Websocket (preferred) and HTTP (with limitations: subscriptions are not supported over HTTP).
Deribit API has public and private methods. The public methods do not require authentication. The private methods use OAuth 2.0 authentication. This means that a valid OAuth access token must be included in the request
Deribit FIX API is a subset of FIX version 4.4, but also includes some tags from 5.0 version and several custom tags. Deribit uses the standard header and trailer structure for all messages.
Empirica’s trading platform has been integrated our trading bots with Deribit API in order to operate Bitcoin trading, so that our customers can use it out of the box. Let’s name some trading bots that can be applied using our trading platform through API integration on Deribit:
- Market Making bot: the service of quoting continuous passive trades prices to provide liquidity, and also be able to make some profits throughout this process.
- Arbitrage bot: takes advantage of small differences between markets. It is a trading activity that makes profits by exploiting the price differences of identical or similar financial instruments on different markets.
- Price mirroring bot: this bot uses liquidity and hedging possibilities from other markets to make the markets in a profitable way.
- Triangular Arbitrage bot: using this bot a trader could use the opportunity of exploiting the arbitrage opportunity from three different FX currencies or Cryptocurrencies.
- Basket Orders bot: with this bot, it is possible to execute trades on multiple coins at the same time with the possibility to hedge against other coins.
- VWAP bot: using this bot a trader can achieve the best price with large order by splitting it into multiple smaller ones throughout the trading day.
- Smart Order Routing bot: with this bot, the trader can find the best price for your order on all crypto exchanges and execute it.
In case you would need help from professional software developers to help you build proprietary trading bots and integrate it with API of Deribit or other crypto exchanges, you can consult with our quant team.
What is market sentiment analysis?
Also known as opinion mining or emotion artificial intelligence, or textual sentiment analysis aims to process and extract the subjective content of descriptive and written work. This objective is achieved through the analysis of the source’s opinions, or their evaluation towards a topic or a product as well as sentiments, attitudes, and emotions.
In trading, investment banks and hedge funds are trying to take advantage of sentiments of the market to help them make better predictions about the financial market. Some of the very accomplished firms including DE Shaw, Two Sigma and Renaissance Technologies have been reported to use sentiment signals. In some of these approaches, sentiment signals are blended in with other data such as transactional data (prices, historical returns or dividends). Sentiment analysis can be performed as stand-alone research prior to traditional trading or it could be equipped into algorithmic trading.
Techniques such as Natural Language Processing and Text Mining are employed in order to perform analysis and the extraction process.
Types of sentiment
Based on the typology proposed by Scherer, there are two types of sentiment while performing sentiment analysis. One type is the “attitude”, which is a narrow definition of sentiment, whether the opinion is “positive” or “negative”.
The second type of sentiment within sentiment analysis is the “emotion”. This indicates the eight “basic emotions”, which are in four opposing pairs, joy-sadness, anger-fear, trust-disgust and anticipation-surprise.
Steps to perform sentiment analysis on market data
Step one: data collection
In order to perform sentiment analysis on market instruments e.g. a stock, there first should be the source to which the data stream flows for sentiment algorithms to start processing. Such data sources are best to be provided through APIs to reach automation in the collection process. One common and widely used source of data for stock market sentiment analysis is Twitter. Through the search query API provided by Twitter, the thrid-party system can query data via the REST API with given conditions such as location, language and time and etc.
Step two: Classifier engine
Once required corpus data created for the sentiment analysis, there should be a step to extract features from the data. Different domains should design classifiers differently since words could mean differently across different industries. Having said that, most of the sentimental words (basically adjectives) are self-explanatory. Some of the methods used to create word classifiers and feature extraction engines are Naive base, Support Vector Machine (SVM) or K-clustering methods.
Step three: predictive modeling
Once the classifier engine is in place, the data scientists will start the process of examining the best predictive models for the data. Which makes this step into two phases, the training, and testing (predicting). during the training phase, the data scientists will train different models and fine-tune them in order to use it for the second phase which is testing which will result in obtaining an accuracy level. This process iterates until there is an acceptable accuracy level determined by domain experts and data scientists. Results of such tests should also be checked if whether they are statistically reproducible or not, basically if the model achieves a similar accuracy level on different datasets throughout time.
Are price and sentiment correlated?
There are numerous researches conducted over this question, nevertheless, the general intuition in the market is that; there is a correlation between sentiment and price in the market. In experimental work with consideration to asses whether the financial market reacts to relevant news events, the sentiment analysis was performed using both a standard model and an enhanced temporal model. Within the temporal test, they associate the sentiments with the corresponding temporal orientations by classifying each sentence with one of the four temporal categories (past/present/future/unknown) and calculate the sentiment strength accordingly.
The result of this practice concluded that the casualty test experiment, two competing hypotheses that market sentiment cause price changes and vice versa were approved. In most research cases it is not clear which is the cause and which is the effect.
Even though the correlation between price and sentiment in the market can be seen but predict the price of stocks is the hard part. Many types of research have been conducted in this field and results are somewhat average.
Though another significant challenge linked to the sentiment analysis approach to the stock market is the notion of opinion expressing words. Some words can be perceived as either positive or negative depending on the context they are used, e.g the word short could be used in latency and indicates a positive message.
by Michal Rozanski, CEO at Empirica
Most wealth managers are in deep denial about robo advice. They say they need human interaction in order to understand the nuances of financial lives of their customers. And their clients value the human touch. They’re wrong. Soon robo advice will be much more efficient than human advice ever was.
In this post, we will share the results of our analysis on the most important areas where the application of machine learning will have the greatest impact in taking wealth management to the next level.
What Artificial Intelligence is and why you should care
“Computers can only do what they are programmed to do.” Let us explain this is huge misconception, which was only valid because of limited processing power and memory capacity of computers. Most advanced programs which mimic specialized intelligences, known as expert systems, were indeed programmed around a set of rules based on the knowledge of specialists within the problem’s domain. There was no real intelligence there, only programmed rules. But there is another way to program computers, which makes them work more similarly to the functions of the human brain. It is based on showing the program examples of how certain problems can be solved and what results are expected. This way computers equipped with enough processing power, memory and storage are able to recognize objects in photographs, drive autonomous cars, recognize speech, or analyse any form of information which exhibits patterns.
We are entering the age where humans are outperformed by machines in activities related with reasoning based on the analysis of large amounts of information. Because of that finance and wealth management will be profoundly changed during the years to come.
Real advice – combining plans with execution
A great area for improvement in finance management is the combination of long term wealth building with the current financial situation of the customer as reflected by his bank account. For robo-advisors, an integration with bank API opens the door to an ocean of data which, after analysis, can dramatically improve the accuracy of advice provided to the customer.
By applying a machine learning capabilities to a customer’s monthly income and expenses data, wealth managers will gain a unique opportunity to combine two perspectives – the long term financial goals of their customers and their current spending patterns. Additionally, there is the potential of tax, mortgage, loans or credit card costs optimization, as well as using information on spending history to predict future expenditures.
By integrating data from social media, wealth management systems could detect major changes in one’s life situation, job, location, marital status or remuneration. This would allow for automated real time adjustments in investment strategies of on the finest level, which human advisors are simply unable to deliver.
New powerful tools in the wealth manager’s arsenal
Hedge funds that are basing their strategies on AI have provided better results over the last five years than the average (source Eurekahedge, more on hedge fund software). What is interesting is that the gap between AI and other strategies has been growing wider over the last two years, as advancements in machine learning accelerated.
The main applications of machine learning techniques in wealth management, can be categorized following cases:
- Making predictions on real-time information from sources such as market data, financial reports, news in different languages, and social media
- Analysis of historical financial data of companies to predict the company’s cash flow and important financial indicators based on the past performance of similar companies
- Analysis of management’s public statements and activity on social networks in order to track the integrity of their past words, actions and results
- Help in accurate portfolio diversification by looking for uncorrelated instruments which match requirements of the risk profile (see portfolio management software)
- Generation of investment strategies parametrized by goals such as expected risk profiles, asset categories, and timespan, resulting in sets of predictive models which may be applied in order to fulfill the assumptions
To give an example of machine learning accuracy, the algorithms for sentiment analysis and document classification are already on acceptable levels, well above 90%.
When it comes to the execution of the actual orders behind portfolio allocation and rebalancing strategies, many robo advisors are automating these processes passing generated orders to brokerage systems through algorithmic trading systems. The next step would be autonomous execution algorithms, that take under consideration the changing market situation and learn from incoming data, allowing for increased investment efficiency and reduced costs.
Machine learning can be applied to quantitative strategies like trend following, pattern recognition, mean reversion, and momentum, as well as the prediction and optimization of statistical arbitrage, and pairs trading. Additionally, there is a possibility to apply machine learning techniques in, already quite sophisticated, execution algorithms (aka trading bots) that help execute large orders by dividing them to thousands of smaller transactions without influencing the market while adjusting their aggressiveness to the market situation.
What’s interesting is that algorithms could also be trained to make use of rare events, like market crashes and properly react in milliseconds, already knowing the patterns of panic behaviour and shortages of liquidity provision.
Explaining the markets
In wealth management systems, if portfolio valuations are provided to the customers in real time, then so should explanations of the market situation. Every time the customer logs in to the robo-advisor, she should see all required portfolio information with a summary of market information relevant to the content of her portfolio. This process includes the selection of proper articles or reports concerning companies from the investor portfolio, classification and summarization of negative or positive news, and delivering a brief overview.
Additionally, machine learning algorithms can be used to discover which articles are read by customers and present only those type of articles that were previously opened and read by the customer.
The result will be not only the increase in customer understanding but also, by providing engaging content to investors, the increase in their engagement and commitment to portfolio strategy and wealth management services.
Talking with robots
The ability to deliver precise explanations of the market situation in combination with conversational interfaces aided by voice recognition technology will enable robo-advisors to provide financial advice in a natural, conversational way.
Voice recognition is still under development, but it could be the final obstacle on they way to redesigning human-computer interaction. On the other hand, thanks to deep learning, chatbot technology and question answering systems are getting more reliable than ever. KAI, the chatbot platform of Kasisto, who has been trained in millions of investment and trade interactions, already handles 95 % of all customer queries for India’s digibank.
Decreasing customer churn with behavioral analysis
The ability to track all customer actions, analyzing them, finding common patterns in huge amounts of data, making predictions, and offering unique insights for fund managers delivers a powerful business tool not previously available to wealth managers. What if nervousness caused by portfolio results or market situation could be observed in user behaviour within the system? This information, combined with the results of investments and patterns of behaviour of other investors, can give a wealth manager the possibility to predict customer churn and react in advance.
When speaking with wealth management executives that are using our robo-advisory solutions, they indicate behavioural analysis as one of the most important advancements to their current processes. Customers leave not only when investment results are bad, but also when they are good if there is a fear that the results may not be repeated in the future. Therefore, the timely delivery of advice and explanations of market changes and the current portfolio situation are crucial.
The same model we used to solve the behavioral analysis problem has been proven to predict credit frauds in 93.07% of cases.
Other areas of applying machine learning in the processes supporting wealth management services could be:
- Security based on fraud detection which actively learns to recognize new threats
- Improving sales processes with recommendations of financial products chosen by similar customers
- Psychological profiling of customers to better understand their reactions in different investment situations
- Analysis and navigation of tax nuances
- Real estate valuation and advice
Implementing these AI functions in wealth management systems will be an important step towards the differentiation of the wealth managers on the market. Today’s wealth managers’ tool set will look completely different in five years. Choosing an open and innovative robo-advisory system that tackles these future challenges is crucial. Equally important will be wealth managers’ incorporation of data analytic processes and the use of this data to help their customers.
Artificial intelligence is poised to transform the wealth management industry. This intelligence will be built on modern wealth management software that combine data from different sources, process it, and transform it into relevant financial advice. The shift from data gathering systems to predictive ones that help wealth managers to understand the data, has already started. And wealth management is all about understanding the markets and the customers.
We write about digital assets, liquidity, defi, leading cryptocurrencies like Bitcoin and Ethereum, crypto exchanges including CEX like Binance, Coinbase or DEX like Uniswap, quantitative algorithms like arbitrage, web3, and blockchain.
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