Štítek: Optimization

[:cs]Optimalizace obchodního systému[:en]Investment strategy optimization[:]

Publikováno: 11. 2. 2019 , , ,

[:en]An investment strategy is basically a list of exact rules (algorithm), which defines investment decisions (buy and sell orders), can analyze and manage real-time risks based on current market situations. Investment decisions can be divided into two groups – entry rules for opening investment positions and exit rules for closing investment positions. Our research department uses a method which takes these two rule categories and applies different development attitude on each of them.

Standard old-fashioned approach

Standard old-fashioned development approach is based on using financial experts‘ rules what depends on experience, knowledge and personal attitude. These entry and exit rules are usually very subjective and may lack a deeper analysis of various datasets (news, fundamentals, technical, psychological). However, it doesn’t mean that an investment algorithm containing these rules is wrong and can’t generate profit in a long-term period. It can be very robust and stable.

New era approach

Our research department loves new technologies which can help to deliver higher and more stable performance, lower drawdowns with a shorter recovery period, better Sharpe ratio and other key performance metrics. Current research is focused on implementing machine learning and genetic programming to investment strategies – specifically to improve the effectiveness of the exit rules. We use a few simple entry rules defined by a financial market expert (old-fashioned approach), but exit rules are generated by modern technologies for deep analysis of different datasets. Exit rules contain simple or sophisticated patterns founded by genetic programming and machine learning, but this whole approach is still under expert control and each rule is validated with strict conditions to avoid over-fitting.

Customers will be able to benefit from these algorithms by using them directly on their own accounts through specialized trading platforms or by investing in the big funds which will use these algorithms.

Vision

Our vision is based on improving human skills with data analysis technologies and deliver higher and more stable performance. We are not trying to take-over human decisions, but we are trying to make them better.

If you are interested in more information stay tuned to our website or get in touch! It would be a pleasure to invite you for an meeting at our office.

Michal Dufek[:]

The Relative Value Approach

Publikováno: 2. 1. 2019 , , ,

Introduction

This post aims to give information our team is currently dealing with – a investment approach used by hedge funds and proffesional portfolio managers. There are such investment approaches that do not require crystal balls to expose investment opportunities, but which are base on statistics, economic operators, logical relationships and the professional use of state-of-the-art technologies. A key fact is that there is no use of the prediction of future values for estimating the future direction of the asset analysed, but the approach seeks anomaly (market opportunities) in the relationships of different asset prices among which there is a certain (economic, political, commercial, technical) logical relationship. The result is a investment strategy which does not estimate the direction of the future development of the asset but which is able to identify the business opportunity created in real time.

Motivation

Our goal is to create/find alpha factor that gives an investor an advantage over a normal approach where an investor buys and holds a security until its maturity or sale. One source of this alpha factor is „Relative Value Trading“, which contains many investment approaches, including Statistical Arbitrage, Convertible Arbitrage, Fixed Income Arbitrage, Equity Market Neutral, Spread Trading, Pair Trading. The point of this approach is to track the logical links among the selected assets (we used 30 randomly selected U.S. shares) and the trading of anomalies that appear over time on those relationships.

Data

A wide range of underlying assets (shares, commodity futures, bonds, government bonds and CDS, convertible bonds and their underlying shares) can be used to analyse the Relative Value.

For our initial analysis, we used 30 randomly selected U.S. shares, which are currently mainly used to compile and verify the correctness of the methodology and operating process.

Once the operating process has been detuned, we will also deploy the application to other data sources.

Methodology

The Relative Value Approach is a mean-reverting investment approach where the underlying premise is a stable relationship between two or more assets.

This relationship is identified by a long-term correlation matrix of differentiations of logarithmic price of the assets entering the analysis.

These short-term anomalies provide an alpha factor on which to build a investment strategy used by proffesional portfolio managers.

 

Table 1: Correlation matrix of significant relationships

XOM JPM GS CVX
XOM 1.000000 0.464594 0.490233 0.879080
JPM 0.464594 1.000000 0.733680  0.489734
GS 0.490233 0.733680  1.000000 0.523985
CVX 0.879080  0.489734 0.523985 1.000000

 

Figure 1: Correlation coeficients heatmap

The chart above illustrate the results of the first step. Of the 30 titles analysed, we identified 2 bilateral long-term relationships formed by JPMorgan Chase & Co. (JPM) with Goldman Sachs Group Inc (GS) and Exxon Mobil Corporation (XOM) with Chevron Corporation (CVX).

In the second step, we culled a short-term sliding correlation with a period of 10 business day, for these two relationships. The goal was to trace short-term deviations from long-term normal which will be further used as a alpha signals for a investment strategy. The results are shown in the charts bellow.

 

Figure 2: Comovements and short-term disruption in relationship „XOM-CVX“

 

Figure 3: Comovements and short-term disruption in relationship „JPM-GS“

On both lower charts you can see short-term decreases in correlation coeficients, which we consider to be signs of short-term deviations from long-term normal. We regard these short-term deviations as a investment opportunity, the automated use of which will our team further address.

Conclusion

This post aims to outline the idea of one of the investment approaches by hedge funds and professional portfolio managers and to show the results achieved by us. Further steps (robustness and stability checks of the long-term correlation matrix) will be analysed in other positions, which we are curently dealing with in the search for optimisation solutions in assets management in detail, including the technological processes.

Michal Dufek