Machine Learning in Finance

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.

Machine Learning layers

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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.

Types-of-chatbots

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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. 

  • Modeling

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. 

  • Evaluation

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. 

  • Deployment

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.

 

Algorithmic Trading – A complete guide

What is algorithmic trading?

 

Algorithmic Trading (also called quantitative or automated trading) in simple words describes the process of using computer programs to automate the process of trading (buying and selling) financial instruments (stocks, currencies, cryptocurrencies, derivatives). These computer programs are coded to trade based on the input that has been defined for them. Inputs could be based on the aimed strategy to take advantage of different market behaviors such as the specific change of a price could trigger the algorithms to make some specific trades, or other factors like volume, time or sophisticated algorithms that trade based on trading indicators.  

Algorithmic trading strategies and backtesting

 

Almost all trading ideas are first converted to a trading strategy and coded into an algorithm that then comes to life and ready for execution. Most algorithmic trading strategies are created on the basis of wide trading knowledge on the financial market combined with quantitative analysis and modeling, later the strategies are given to quants programmers who will convert the strategy to executable algorithms. 

 It is widely common to perform testing on trading strategies before they go live on the market, this practice is known as Backtesting. This is where the algorithm is being tested on historical data to check the algorithm and apply further modifications. 

The main idea behind Backtesting is to evaluate the performance of the algorithmic strategy to see if the strategy is behaving the way it was programmed and check the profitability of it using real market data. 

For more sophisticated algorithms and firms with more advanced tools, algorithmic strategies perform on what so-called paper trading, where the strategy performs virtual trading without committing any commercial value (trading without money). 

The most popular programming languages used to write automated trading strategies are JAVA, Python, and C++. Matlab is also a good tool with a wide range of analytic tools to plot and analyze algorithmic strategies.

Who uses algorithmic trading?

 

By far the most common fans of performing trades algorithmically are larger financial institutions as well as investment banks alongside Hedge Funds, pension funds, broker-dealer, market makers.

Some well-known algorithmic strategies

 

On a broad sense most commonly used algorithmic strategies are Momentum strategies, as the names indicate the algorithm start execution based on a given spike or given moment. The algorithm basically detects the moment (e.g spike) and executed by and sell order as to how it has been programmed. 

One another popular strategy is Mean-Reversion algorithmic strategy. This algorithm assumes that prices usually deviate back to its average. 

A more sophisticated type of algo trading is a market-making strategy, these algorithms are known as liquidity providers. Market Making strategies aim to supply buy and sell orders in order to fill the order book and make a certain instrument in a market more liquid. Market Making strategies are designed to capture the spread between buying and selling price and ultimately decrease the spread. 

Another advanced and complex algorithmic strategy is Arbitrage algorithms. These algorithms are designed to detect mispricing and spread inefficiencies among different markets. Basically, Arbitrage algorithms find the different prices among two different markets and buy or sell orders to take advantage of the price difference. 

Among big investment banks and hedge funds trading with high frequency is also a popular practice. A great deal of all trades executed globally is done with high-frequency trading. The main aim of high-frequency trading is to perform trades based on market behaviors as fast and as scalable as possible. Though, high-frequency trading requires solid and somewhat expensive infrastructure. Firms that would like to perform trading with high frequency need to collocate their servers that run the algorithm near the market they are executing to minimize the latency as much as possible.

Adaptive Shortfall

Adaptive Implementation Shortfall algorithm designed for reduction of market impact during executing large orders. It allows keeping trading plans with automatic reactions to price liquidity.

Basket Trading

Basket Orders is a strategy designed to automated parallel trading of many assets, balancing their share in the portfolio’s value.

Bollinger Band

Bollinger bands strategy is a trading algorithm that computes three bands – lower, middle and upper. When the middle band crosses one of the other from the proper side then some order is made.

CCI

Commodity channel index strategy is a trading algorithm which actions are dependent on the value of a CCI index which bases on average and variance of some number of last trades.

MACD

MACD strategy is a trading algorithm which actions are dependent on two lines of MACD and the MACD Signal Line calculated with EMA.

Market Close

The strategy is designed to reduce costs interrelated with the market impact of huge orders. It works until the demanded time and may take advantage of the auction on Market Close.

Parabolic SAR

Parabolic SAR strategy is a trading algorithm whose role is to predict market trend change and trade assets in specific market conditions.

POV

Percent of Volume (POV) is a trading algorithm based on volume used to the execution of bigger orders without excessive impact on the market price.

RSI

Relative strength index strategy is the trading algorithm which actions are dependent on the value of an RSI index which bases on average wins and losses of a strategy.

Slow Stochastic Oscillator

Slow Stochastic Oscillator Strategy is build to gain profit on buying/selling shares in specific market conditions.

Statistical Arbitrage

Statistical Arbitrage (SA) is build to gain profit on simultaneously buying and selling two shares of two correlated instruments.

TWAP

Time-Weighted Average Price (TWAP) is a trading algorithm based on the weighted average price used to the execution of bigger orders without excessive impact on the market price.

VWAP

Volume-Weighted Average Price (VWAP) is a trading algorithm based on a pre-computed schedule that is used in the execution of a bigger order without an excessive impact on the market price.

Williams %R

Williams %R strategy is a trading algorithm basing on trend change indicated by Williams %R oscillator. Oscillator leads the strategy to set a long or short position.

Smart Order Routing

Technically Smart Order Routing technology will search for available liquidy across given trading venues, and with mid-point matching will get the best possible chance of price improvements.

Triangular Arbitrage 

Triangular Arbitrage is used when a trader would like to use the opportunity of exploiting the arbitrage opportunity from three different FX currencies or Cryptocurrencies. Triangular Arbitrage happens when there are different rates within the trading venue/s.

Algorithmic Trading Software

 

Based on the given use case like the size of orders, customizability and experience level there are options available for algo trading software. Larger firms like hedge funds, investment banks or proprietary trading firms use rather more tailored custom-built and advanced tools. When it comes to more individual traders or quants with less capital to trade they will rather use more readymade algorithmic strategies, some on the cloud, some stand-alone. 

The most common features of algorithmic trading software are ways to analyze profit/loss of an algorithm on a live market data. There are different protocols available to get, process and send orders from software to market, such as TCP/IP, webhooks, FIX and etc. One important factor for this data processing from receiving to processing and pushing order is measured with latency. Latency is the time-delay introduced to the movement of data from points to points. Considering the changes in price in the market the lower obtained latency the better software reacts to market events hence a faster reaction. 

Backtesting is another useful feature that should be included in algo trading software, usually, this software allows traders to apply their automated trading strategies and test it with historical data to evaluate the profitability of their strategies. 

Pros and cons of algorithmic trading

 

Just like any other choice, there are pros and cons to using algorithmic trading strategies and automating the process of trading. Let’s get down with the pros. Based on many expert opinions in investments human emotions could be toxic and faulty when it comes to trading, one perhaps most acknowledged pros of quantitative Trading is taking away human emotions and errors out of trading.

Another huge advantage of algorithmic trading is the increase of speed in action of execution to the market as well as possibilities to test strategies using Backtesting and paper-trading in a simulated manner. Testing quantitative strategies determine the viability of the idea behind trading strategies.

Another vastly discussed advantage of quantitative trading is risk diversification. Algorithmic trading allows traders to diversify themselves across man accounts, strategies or markets at any given time. The act of diversification will spread the risk of different market instruments and hedge them against their losing positions. 

Making trading automatically using quant trading decreases the operational costs of performing large volumes of trade in a short period of time. 

There are also a few other advantages such as automation in the allocation of assets, keeping a consistent discipline in trading and faster execution.

Now let’s get on with some of the cons of using algorithmic trading. Perhaps one very discussed issue with using algorithmic trading is constant monitoring of the strategies which to some traders could be a bit stressful since the human control in automated trading is much less. Though it is widely common to have lost control features included in strategies and algorithmic trading software (automated and manual ones). 

For most individual traders having enough resources could be another disadvantage of Algorithmic trading. The automated trading reduces the cost of executing large orders but it could come expensive as it requires initial infrastructure such as the software cost or the server cost.

 

Pros Cons
Emotionless trading Needs for monitoring 
Less error Technological infrastructure 
Higher trading speed Programming skills required for updating strategies
Backtesting and paper trading
Risk diversification 
Lower operational costs 
Consistent trading discipline 

 

Performance

 

There are different performance results depending on the basis on which an algorithmic trading strategy is built. Though, as an example, an algorithmically managed fund in 2018 (during which S&P500 index was 19.42% high) SH capital partners posted 234.09% returns. Over the same period, Silver8 Partners and Global Advisors Bitcoin Investment Fund achieved 770.75% and 330.08% returns respectively.  Both of these algorithmic trading examples are automatically traded but differ on specific strategies – however, both attribute their success to their Automatic trading winning strategies and their rationale on digital assets.

Even most profitable algorithms with reasonable levels of volatility (eg. a Sharpe ratio of 2+ and max drawdowns of <5–10%) have a limited shelf life because any Algo that produces consistent greater-than-market returns will suffer from alpha decay (the erosion of edge due to others getting in on the action).

In order to provide a better view of performance statistics, we have prepared results from Reinsseance hedge fund, see the table below:

The are some perfromance statistic from the Reinsseance trading firm:

 

Year  Net return Management fee Performance fee Returns before fees Size of the fund Medallion trading profits 
1988 9.0% 5% 20% 16.3% $20 million $3 million 
1989 -0.4% 5% 20% 1.0% $20 million $0
1990 55.0% 5% 20% 77.8% $30 million  $23 million
1991 39.4% 5% 20% 54% $42 million $23
1992 33.6% 5% 20% 47% $74 million $35 million
1993 39.1% 5% 20% 53.9% $122 million $66 million
1994 70.7% 5% 20% 93.4% $276 million $258 million
1995 38.3% 5% 20% 52.9% $462 million $244 million
1996 31.5% 5% 20% 44.4% $637 million $283 million
1997 21.2% 5% 20% 31.5% $829 million $261 million 
1998 41.7% 5% 20% 57.1% $1.1 billion $261 million
1999 24.5% 5% 20% 35.6% $1.54 billion  $549 million
2000 98.5% 5% 20% 128.1% $1.9 billion 2,434 million
2001 33.0% 5% 36% 56.6% $3.8 billion 2,149 million
2002 25.8% 5% 44% 51.1% $5.24 billion 2.676 billion
2003 21.9% 5% 44% 44.1% $5.09 billion $2.245 billion
2004 24.9% 5% 44% 49.5% $5.2 billion $2.572 billion
2005 29.5% 5% 44% 57.7% %5.2 billion $2.572 billion
2006 44.3% 5% 44% 84.1% $5.5 billion $4.374 billion
2007 73.7% 5% 44% 136.6 $5.2 billion $7.104 billion
2008 82.4% 5% 44% 152.1% $5.2 billion  $7.911 billion
2009 39.0% 5% 44% 74.6% $5.2 billion $3.881 billion
2010 29.4% 5% 44%  57.7% $5.2 billion $3.881 billion
2011 37.0% 5% 44% 71.1% $10 billion $7.107 billion
2012 29.0% 5% 44% 56.8% $10 billion $5.679 billion
2013 46.9% 5% 44% 88.8% $10 billion $8..875 billion
2014 39.2% 5% 44% 75.0% $9.5 billion 7.125 billion
2015 36.0% 5% 44% 69.3% $9.5 billion $6.582 billion
2016 35.6% 5% 44% 68.6% $9.5 billion $6.514 billion
2017 45.0% 5% 44% 85.4% $10 billion $8.536 billion 
2018 40.0% 5% 44% 76.4% $10 billion 7.643 billion

Source: The man who solved the market, how Jim Simons launched the quant revolution, by Gregory Zuckerman

The Reinsurance hedge fund in total achieved 39.1% net returns, 66.1% average returns before fees and in total $104,530,000,000 total trading profits. 

Algorithmic trading in Cryptocurrency

 

Unlike more mature instruments like stocks, options or CFDs, the Cryptocurrency market is quite volatile. Typically higher volatility leads to more frequent jumps in the price of instruments, higher and lower. Hence, some professional traders find this amusing and opportunistic to make the most of the profits.  

Generally, for Cryptocurrency traders, there are plenty of cloud-based solutions using trading bots, though for very professional and institutional traders this may not flexible enough. There are few automated trading platforms for cryptocurrencies which can utilize the need for more sophisticated and institutional traders. 

 

Quantitative Trading Trends

 

On average 80% of the daily traders across the US are done by algorithmic trading and machines. Though the volume of automated trading can change based on the volatility in the market. According to J.P. Morgan, fundamental discretionary traders are accounted for only 10% of trading volume in stocks. This is the traditional way of checking the companies business performance and their outlook before deciding whether to buy or sell a position. 

The growth in the number of algorithmic trading since last year comes close to 47% and there is 41% growth in the number of users executing their trades algorithmically. Mobile also plays an important role in the tools provided there is around 54% growth in trading FX algorithmically using mobile devices. 

New technologies, Artificial Intelligence, Machine Learning, Blockchain

 

According to another J.P. Morgan research, Artificial Intelligence and Machine learning are predicted to be the most influential for shaping the future of trading. Based on this analysis Artificial Intelligence and Machine Learning will influence the future of trading by 57% and 61% in the next three years.  

Interestingly this report states that Natural Language Processing alone will count to 5% of the change in the next 12 months and up to 9% in the next three years. 

J.P. Morgan’s report shows that around 68% of the traders believe that Artificial Intelligence and Machine Learning provide deep data analytics. Around 62% believe that Artificial Intelligence and Machine Learning optimize trade execution and 49% of traders believe that Artificial Intelligence and Machine Learning represents an opportunity to hone their trading decisions. 

The same report indicates that Blockchain within the next 12 months will influence the trading up to 9% and 19% within the next three years. Within the same report, the usage of mobile trading applications is to influence the trading market up to 28% within the next 12 months and 11% within the next 3 years. 

 

Market share

 

Morgan Stanley estimated in 2017 that algorithmic strategies have grown at 15% per year over the past six years and control about $1.5 trillion between hedge funds, mutual funds, and smart beta ETFs. Other reports suggest the quantitative hedge fund industry was about to exceed $1 trillion AUM, nearly doubling its size since 2010 amid outflows from traditional hedge funds. In contrast, total hedge fund industry capital hit $3.21 trillion according to the latest global Hedge Fund Research report.

Empirica among innovative companies at the Trading CEE conference

The “Trading CEE: Equities and Derivatives” conference is one of the most important financial industry related events in Central and Eastern Europe. The co-organizers of the event were the Warsaw Stock Exchange, the Global Investor Group and the National Depository for Securities. Michał Różański, CEO of Empirica took part in a panel devoted to the future of the fintech industry.

The Trading CEE was held in Warsaw’s Hilton hotel, where several hundred capital markets experts had the opportunity to talk about such important issues as the Mifid II regulation, or the scale of the fintech revolution in Poland and internationally.

They also discussed the decision made recently by FTSE Russell (the supplier of indices belonging to the London Stock Exchange group) to change the status of Poland from that of an Emerging Market into that of a Developed Market and considered the significance of this shift for the national economy.

Among many of the excellent speakers, we had the change to listen to Marek Dietl, President of the Warsaw Stock Exchange and Toby Webb, Head of EMEA Information Services FTSE Russell. The inaugural panel on the opportunities and threats facing investment markets in our region gathered such experts as Ales Ipavec, head of the stock exchange in Ljubljana, Richard Vegh from the Budapest Stock Exchange, Ivan Takev, head of the Bulgarian Stock Exchange and Head of International Sales of the Moscow Stock Exchange Tom O ‘ Brien.

Fintech Innovation Forum

The panel regarding the fintech industry was very popular among visitors, especially the topic of the development of tools based on artificial intelligence and their impact on investment markets in Poland. It was organized in such a way as to allow for 4 of the most promising Central & Eastern European companies in the modern financial technologies industry to present what they offer. One of the main participants of this part of the Trading CEE conference was Michał Różański, CEO and founder of Empirica, the fintech software house.

During his speech, he focused mainly on the presentation of innovations in the field of robo-advisors, which are already revolutionizing the global investment market.

– The robo-advisor platform is not only the future, but the present of wealth and asset management. Our Empirica Robo Advisor service stands out in the international market above all through its very high level of support for advisors in their work with the service’s users. All this is thanks to solutions in the field of AI analytics, which allows them to receive a full picture of the actions taken in the user profile and to quickly respond if these actions threaten the assets, which in the end also reduces the risk of losing the customer. Another important element of our consulting service is the fact that we have built it based on the strong foundations of our platform for Algo Trading. Thanks to it, our robo-solution has fully automated access to the data stream coming from the most important financial institutions at every stage of the Empirica Robo Advisor process. – explains Michał Różański.

New generation of users

Platforms from the robo-advisor category not only democratize investment opportunities, but also reduce the price of consultancy services. In an era of technological revolution, a millennial generation is slowly entering the capital market- people accustomed to continuous presence in the online world. Advisory platforms will enable it for them. Friendly user interfaces, notifications that they know from social media and an automated transaction system based on a personalized portfolio are already present in the fintech area. However, in order for these tools to function in such a complicated environment as the financial market, powerful computational engines based on artificial intelligence (AI) must be behind them. Empirica helps financial companies enter this world by providing an advisory platform that automates the asset management processes and is based on innovative solutions in the field of data processing. – adds the CEO of Empirica.

 

Empirica is a Wrocław-based company that offers solutions such as an Algo Trading Software implemented by major institutional investors in Poland, market makers software, wealth management system framework, cryptocurrency trading bots and trading software development services for companies from capital and cryptocurrency markets.

Do traditional exchanges see Blockchain as an opportunity?

Distributed ledgers technology also known as Blockchain, offers a new way to data management and sharing that is being used to propose solving many inefficiencies affecting the financial industry. Technology experts, Fintech start-ups, banks and market infrastructure providers are working on underlying technologies and its potential use in the industry. However the journey of such transformation may take long. In this post we will focus on the benefits and architectural changes Blockchain could bring to capital market, and some example from such appliances across exchanges around the world.

The potential benefits of Blockchain technologies could cover different process within different stages in capital markets. In order to expose why capital markets would pursue to Blockchain technologies its worth taking a look at the benefits across pre-trade, trade, post-trade and security servicing.

Pre-trade:

Blockchain technology will establish more transparency on verification of holdings. Additionally it reduces the credit exposure and making Know-your-customer way simpler.

Trade:

For this stage, Blockchain technologies provide a more secure, real-time transaction matching and a prompt irrevocable settlement. Blockchain could also help automating the reporting and more transparent supervision for market authorities, we could add higher standards for anti-money laundering.

Post-trade:

In this regard it eliminates the demand for central clearing for real time cash transactions, reducing collateral requirements. Blockchain technology enables quicker novation and effective post-trade processing.

Securities and custody servicing:

Distributed asset ledgers with flat accounting structures could remove some of the role which custodians and sub-custodians play today. Custodians’ function might change to that of a ‘keeper of the keys’, managing holdings data and ensuring automatic securities servicing operations are done correctly. To that end we could also add advantages such as common reference data, simplification of fun servicing, accounting, allocation and administration.

Nasdaq has become the forefront of blockchain revolution, they have and are currently involved with many blockchain jobs. To name these endeavors, it started with Nasdaq Linq blockchain ledger technology. Linq is the primary platform in a recognized financial services firm to show how asset trading could be managed digitally through the usage of blockchain-based platforms. Nasdaq has continued more to blockchain, showing that, it is working to develop a trial utilizing the Nasdaq OMX Tallinn Stock Exchange in Estonia which will discover blockchain technology being used as a way to reduce obstacles preventing investors by engaging in shareholder voting. The intention is to boost efficiency in the processing of purchases and sales of fund units and also to make a device ledger — a place which currently is primarily characterized by manual patterns, longterm cycles and newspaper driven processes.

Read more about Nasdaq activities in Blockchain here.

London Stock Exchange developed to simplify the tracking and management of shareholding information, the new system plans to make a distributed shared registry comprising a list of all shareholder trades, helping to open up new opportunities for investing and trading.

Read more about LSE and IBM activities in Blockchain here.

Australian Securities Exchange (ASX), is all about the replacement of this system that underpins post-trade procedures of Australia’s money equity marketplace, known as CHESS (the Clearing House Electronic Subregister System). ASX is working on a prototype of a post-trade platform for the cash equity market using Blockchain. This initial phase of work was completed in mid-2016. In December 2017 ASX completed its own analysis and assessment of the technology which included:

  • Comprehensive functional testing of the critical clearing and settlement functions currently performed by CHESS
  • Comprehensive non-functional testing (scalability, security and performance requirements) for a replacement system when deployed in a permissioned private network
  • A broad industry engagement process to capture users input on the desired features and functions of a replacement solution
  • Third party security reviews of the Digital Asset DLT based system.

Read more about ASX procedure here.

The Korea Exchange (KRX), South Korea’s sole securities market operator, has established a new service where equity shares of startup businesses may be traded on the open marketplace. The Coinstack platform will offer record and authentication options for your KSM by checking against client references which have already been provided to the platform by Korean banks such as JB Bank, KISA, Lottecard, Paygate in addition to others.

Deutsche Börse Group has developed a theory for riskless transfer of commercial bank funding through an infrastructure based on distributed ledger technology. By combining blockchain technology using its proven post-trade infrastructure, Deutsche Börse aims to achieve efficiencies while at exactly the same time investigating possible new business opportunities enabled by this technology.

Read more about Deutsche Börse Group activities in Blockchain here.

Japan Exchange Group: IBM had teamed up with Japan Exchange Group, which works the Tokyo market, to begin experimenting with blockchain technology for clearing and other operations. IBM says it expect the technology will reduce the cost, complexity and speed of settlement and trading procedures.

About us

Empirica is a Wrocław-based company that supports many local IT initiatives. Empirica is offering solutions such as Automated Cryptocurrency Trading Software implemented by major institutional investors in Poland, market making software, portfolio management system framework, crypto trading bots and trading software development for companies from capital and cryptocurrency markets.