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How Artificial Intelligence will revolutionize wealth management

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%.

Automated execution

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.

Summary

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.

 

 

Bitcoin and Arbitrage: hand in hand

Liquidity, the greatest challenge for crypto exchanges

There is a general consensus that liquidity is the most important factor for all tradable markets. The ability or lack thereof, of a market to allow assets to be bought and sold at stable prices, is a major issue associated with cryptocurrencies. 

According to a recent Encrybit report, one in every three investors is worried about the problem of liquidity on crypto exchanges.

 

The importance of the liquidity problem requires tools and methods to manage markets liquidity. This document proposes an approach to monitor and manage liquidity. Monitoring is intended for the exchange management to understand their platform’s current liquidity level and how to improve it. Liquidity management starts with the exchange engaging professional market makers or using proper tools to take care of  liquidity. Last but not least exchanges should track the market impact of trades of different sizes, and oblige their market makers to fulfil certain conditions.

 

Empirica brings experience, tools, know-how and best practices in the area of technology for liquidity analytics and liquidity provision from capital markets to digital assets. We have been active in the market since 2011, working with stock exchanges and market makers with a track record on automated liquidity provision and measurement. 

 

How can an exchange manage its liquidity

 

Both for those who are just launching a new exchange or who have been operating an exchange for some time already, it is crucial to monitor liquidity metrics of all markets.

 

Read more about our tool for monitoring crypto exchange quality – Liquidity Analytics Dashboard

 

As in any tradable market, liquidity is provided by market makers, who mostly use automated market making algorithms. However, crypto exchanges have an alternative to the external market makers, as they are able to take this crucial aspect of exchange – the provision of liquidity – into their own hands.

 

Regardless of whether they use external market makers or an internal market making desk, crypto exchanges should outline to the liquidity providing party certain conditions pertaining to how the liquidity is provided and then constantly monitor the execution of these obligations. 

 

With proper tools, exchanges are able to track liquidity metrics and are able to react accordingly if agreed conditions are not met. Analytic tools also allow exchanges to compare liquidity in their markets to other crypto exchanges.

 

Monitoring liquidity

 

When executing a transaction, most investors only consider explicit transaction costs (taxes, commissions, fees). But that is only a part of the total cost. The larger the trade, the more dominant the part of the cost taken over by implicit costs.

 

Total transaction costs = Explicit transaction costs + Implicit transaction costs

 

One of the most important implicit costs to consider is market impact, also referred to as slippage. Market impact is a result of the price slipping down or edging up when you trade an asset. As the investor can not execute the entire order at the best offer, the trade is moved down the order book.

 

Exchanges, which want to attract not only small but also bigger investors, should monitor market impact and other important liquidity metrics in all of their markets.

 

Liquidity provision

 

To increase liquidity, crypto exchanges use market making services from external parties. This is a standard practice in any financial market.

 

Market makers

 

A market maker is a company or individual that regularly buys and sells financial assets at a publicly quoted price to provide liquidity to the markets. Their role is to satisfy market demand.

 

Crypto exchanges need market makers. If liquidity is low on a venue, exchanges usually try to attract market makers by the following methods:

  • Decreasing maker trading fees
  • Sharing profit from taker fees
  • Paying market makers for their activity

 

It’s  a “chicken or egg” problem. New exchanges and exchanges with low liquidity need market makers to attract other investors. The market makers, however, do not want to enter illiquid markets as there is not much volume to be made from takers and there is also additional business risk involved. Hence many illiquid exchanges need to pay market makers for their services. 

 

While working with crypto exchanges we often hear multiple reasons as to why crypto exchanges are not happy with their market makers. The main problems include:

  • Market makers choosing to support trading pairs that are most liquid; they are not interested in making markets on less liquid pairs
  • Spreads maintained by market makers are too wide
  • Market makers come and go in the markets that they promise to take care of, so exchanges would like to have tools tracking the activity of their liquidity providers
  • Market makers do not keep the order sizes as promised

Liquidity provision tools for crypto exchanges

 

Crypto exchanges have an alternative to market makers, or a complementary approach. They are able to run an automated market making desk themselves. In order to do that, though, they need funds, proper liquidity provision algos and a trader to monitor them.

 

Market making requires a good combination of technology and some trading skills. The algos must be low-latency and capable of scaling to thousands of orders per second, on numerous trading pairs. It needs a disciplined approach to trading and risk management. 

 

There are many market making tools on the market. They range from simple black-box bots to sophisticated algorithmic engines with market making capabilities.

 

When searching for self liquidity provision tools one should be considering the following criteria:

 

  • Reliability

 

Market making algorithms should work 24/7, and be able to recover from unexpected situations like connection problems with an exchange.

 

  • Security

 

Market making systems have  access to the funds of the exchange, so it is important to choose from proven solutions.

 

  • Transparency

 

In the case of black-box algorithms, the bot developers should be widely known in the community. Exchanges should consider skipping bots and going for proven institutional-grade market making solutions available on the market.

 

  • Parametrization

 

In the case of algorithmic market making it is good practice to choose solutions that enable parametrization and tuning up of execution according to the current market situation.

  • Access to source code and custom changes

Ideally crypto exchanges should have an option to take over the market making algorithms source code and let their team develop and tune it further. Very often exchanges might want to add secret sauce to the algorithms that will create their competitive advantage in the market. 

 

Read more about our tool for market making strategies for crypto exchanges  – Liquidity Engine

 

Competing with other exchanges is a challenge today. In July 2019 services like CoinMarketCap or coinpaprika listed about 260 exchanges. However, Empirica’s internal research shows that there are currently more than 600 crypto exchanges in various stages of maturity, and further new exchanges being launched every month. Every exchange is trying to attract new investors, but it is clear that at some point only those exchanges with the best liquidity will survive. That is why crypto venues should not only manage their own liquidity but also observe the liquidity level of their competition, and identify inefficiencies that can be addressed.

 

About us

Empirica is a trading software company that specializes in liquidity measurement and liquidity provision software that can help exchanges manage their liquidity. Empirica is offering solutions such as Algorithmic Trading Platform used by professional cryptocurrency investors, crypto market makersrobo advisory systemcrypto trading bots and cryptocurrency exchange software development services.

 

The biggest market maker in Poland uses Empirica Platform

It’s been two years since Empirica has successfully deployed the full version of Algorithmic Trading Platform for Dom Maklerski Banku Ochrony Środowiska (DM BOS Brokerage House). Since then DM BOS has become the most active market maker on Polish capital market, running its market making and algorithmic trading operations through Empirica’s platform.

With a great pleasure Empirica would like to inform that DM BOS was lately awarded as Polish Capital Market Leader 2016 by Warsaw Stock Exchange (WSE).

The Gala was attended by representatives of the most important capital market institutions: issuers, brokerage houses, banks, investment firms, industry organisations and associations. DM BOS was awarded in following categories:

 

  • for the biggest share of a local market maker in trading in equities on the Main Market in 2016,
  • for the biggest share of a market maker in the volume of trade in index options in 2016 on the derivatives market,
  • for high quality of the reporting of trades to KDPW_TR in 2016.

– We are very pleased to see, that using our software DM BOS was awarded by WSE in main categories. I would like to sincerely congratulate managers of DM BOS such amazing results for the 2016. Since 2012, when we started our cooperation we are committed to continuously develop and enhance our algorithmic trading platform in order to achieve the highest technical requirements and to satisfy different and changing needs of our customer. I would like to thank DM BOS once again for the opportunity to be a part of their winning market making strategy. – Michal Rozanski, CEO of Empirica

WSE is the biggest securities exchange in Central and Eastern Europe and organises trading on one of the most dynamically growing capital markets in Europe. WSE operates a regulated market of shares and derivative instruments and the alternative stock market NewConnect for growing companies. WSE is developing Catalyst, a market for issuers of corporate and municipal bonds, as well as commodity markets. Since 9 November 2010, GPW is a public company listed on Warsaw Stock Exchange.

About us

Empirica is a trading software company focused on developing the potential that cryptocurrencies bring to financial markets. Empirica is offering solutions such as Algo Trading Platform used by professional cryptocurrency investors, market maker software, robo advisory softwarecrypto trading bots and trading software development services for companies from capital and cryptocurrency markets.

Free version of Algorithmic Trading Platform for retail investors

We have just released beta of Empirica – Algorithmic Trading Paltform for retail investors! It’s lifetime free for development, testing and optimizing of trading algorithms.

Our development team (exactly this team who implemented the entire system) also provides full support in algorithms development as well as connectivity to brokers. If you need help just contact us.

Among many features what is unique is our exchange simulation where you can influence market conditions under which you test your algorithms. No others software offers such a realistic level of simulation.

In paid versions we offer the execution of algorithms in robust server side architecture.

We strive for your feedback!

Best regards,

Michal Rozanski
Founder and CEO at Empirica
twitter: @MichalRoza
https://empirica.io


Empirica Trading Platform – https://empirica.io

Our platform implemented by large brokerage house!

Empirica has successfuly finished the implementation of its Algorithmic Trading Platform in one of the largest brokerage houses in Poland.

Brokerage house will use our software to:

  • aid its internal trading operations, like market making of derivatives on Warsaw Stock Exchange
  • offer functionalities of our platform to its institutional clients, which will be able to build, test and execute their own algorithmic trading strategies

Implementation included connecting of our software system directly to the system of Warsaw Stock Exchange (Universal Trading Platform delivered by NYSE Technologies), as well as the integration with transaction systems of brokerage house. Additionally we have fulfilled and successfuly passed tests regarding the highest security, stability and performance requirements.

This implementation is an important milestone for our system. The usage by team of market makers is a proof that our system is capable of performing high-throughput and low latency operations on level required by most sophisticated traders on the capital marketets.

 

Next release of our algorithmic platform. Version 1.3.4 – has code name “The Firebolt”.

Next implementations of our Algorithmic Trading Platform by customers don’t stop us from developing the platform itself. Working agile requires us to keep the pace in short and frequent iterations, which in case of product means frequent releases, keeping the whole product line stable.

A few iterations that we planned in our 1.3.4 release, code named by our developers ‘The Firebolt’, will include among others:

– even faster real-time replication of all server-side components in master-slave mode (for deployment in larger institutions)

– extended client side backtesting capabilities

– sophisticated charting of backtesting results and statistics

– multiscreen mode of client side application

– additional web-based server-side module for administration & management

 

For those curious about the release name and unfamiliar with Harry Potter, Firebolt is:

“The state-of-the-art racing broom. The Firebolt has unsurpassable balance and pinpoint precision. Aerodynamic perfection.”
—Harry Potter: Quidditch World Cup

“The Firebolt has an acceleration of 150 miles an hour in ten seconds and incorporates an unbreakable Braking Charm. Price upon request.”
—Harry reads about the features of the Firebolt.

Speed, precision, balance, perfection. These are the words that describe our software, therefore choosing the code name was kind of obvious :).

 

The Firebolt broom

Warsaw Stock Exchange certifies our Trading Platform

 

Empirica’s Algorithmic Trading Platform has successfully passed the XDP protocol communication certification, issued by the Warsaw Stock Exchange.

From now on Empirica is officially listed as the ISV (Independent Software Vendor) for the Warsaw Stock Exchange.

WSE uses Universal Trading Platform delivered by NYSE Technologies. The same system is used by many other European and world stock exchanges. Fulfilment of technical criteria of Warsaw Stock Exchange makes certification for those markets only a formality for our platform.

Empirica in the press – ‘The age of robots … ‘

On the first of July 2014 large polish economic magazine Puls Biznesu published an article “The age of robots comes to Warsaw Stock Exchange’. Article is quoting, among others, Empirica’s representatives speaking on the topic of the growth of algorithmic trading in Poland. Excerpts below.

‘Popularization of algorithmic trading on conferences like this one is step in good direction, says Michal Rozanski CEO of Empirica, a company which delivers Algorithmic Trading Platform. Expert says that computers will never replace a human in all the tasks. First and the foremost machines are taking over the processes that human traders had to perform manually. ‘I am sure that the development of algorithmic trading will not change the soul of the markets. It will not change to the race of engineers. It is and always has been the race on new, better ideas.’ says Michal Rozanski. 

 In his opinion both small and big investors will benefit. ‘Appliance of algorithmic trading tools increases liquidity and descreases bid/ask spreads which in turn decreases transaction cost born by all investors’ adds expert.

Michal Rozanski stresses that appliance of algorithmic trading does not limit to transactions with shortt time horizon, e.g. counted in miliseconds. Each trader can designs algorithms adjusted for it’s own requirements. ‘Let’s imagine an investor who would like to open a large position on KGHM shares or futures on WIG20. To make it happen it’s best to divde the order to tens or hundreds of smaller orders, which allows to hide her intentions from other market participants. Investor remains anonymous and minimizes market impact of her large order.’ explains Michal Rozanski. 

‘I am convinced that development of algorithmic trading can be a breakthrough moment in the history of our market, as long as we will treat the matter seriously and deliberately. On Wall Street share of algorithms in total turnover is estimated at 50%, in Europe at 40%, and in Poland still at below 20%. ‘ says Adam Maciejewski, CEO of Warsaw Stock Exchange.

Link to article…

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