Survey of quantitative investment strategies in the Russian stock market : Special interest in tactical asset allocation

Russia’s financial markets have been an uncharted area when it comes to exploring the performance of investment strategies based on modern portfolio theory. In this thesis, we focus on the country’s stock market and study whether profitable investments can be made while at the same time taking uncertainties, risks, and dependencies into account. We also pay particular interest in tactical asset allocation. The benefit of this approach is that we can utilize time series forecasting methods to produce trading signals in addition to optimization methods. We use two datasets in our empirical applications. The first one consists of nine sectoral indices covering the period from 2008 to 2017, and the other includes altogether 42 stocks listed on the Moscow Exchange covering the years 2011 – 2017. The strategies considered have been divided into five sections. In the first part, we study classical and robust mean-risk portfolios and the modeling of transaction costs. We find that the expected return should be maximized per unit expected shortfall while simultaneously requiring that each asset contributes equally to the portfolio’s tail risk. Secondly, we show that using robust covariance estimators can improve the risk-adjusted returns of minimum variance portfolios. Thirdly, we note that robust optimization techniques are best suited for conservative investors due to the low volatility allocations they produce. In the second part, we employ statistical factor models to estimate higher-order comoments and demonstrate the benefit of the proposed method in constructing risk-optimal and expected utility-maximizing portfolios. In the third part, we utilize the Almgren–Chriss framework and sort the expected returns according to the assumed momentum anomaly. We discover that this method produces stable allocations performing exceptionally well in the market upturn. In the fourth part, we show that forecasts produced by VECM and GARCH models can be used profitably in optimizations based on the Black–Litterman, copula opinion pooling, and entropy pooling models. In the final part, we develop a wealth protection strategy capable of timing market changes thanks to the return predictions based on an ARIMA model. Therefore, it can be stated that it has been possible to make safe and profitable investments in the Russian stock market even when reasonable transaction costs have been taken into account. We also argue that market inefficiencies could have been exploited by structuring statistical arbitrage and other tactical asset allocation-related strategies.Venäjän rahoitusmarkkinat ovat olleet kartoittamatonta aluetta tutkittaessa moderniin portfolioteoriaan pohjautuvien sijoitusstrategioiden käyttäytymistä. Tässä tutkielmassa keskitymme maan osakemarkkinoihin ja tarkastelemme, voidaanko taloudellisesti kannattavia sijoituksia tehdä otettaessa samalla huomioon epävarmuudet, riskit ja riippuvuudet. Kiinnitämme erityistä huomiota myös taktiseen varojen kohdentamiseen. Tämän lähestymistavan etuna on, että optimointimenetelmien lisäksi voimme hyödyntää aikasarjaennustamisen menetelmiä kaupankäyntisignaalien tuottamiseksi. Empiirisissä sovelluksissa käytämme kahta data-aineistoa. Ensimmäinen koostuu yhdeksästä teollisuusindeksistä kattaen ajanjakson 2008–2017, ja toinen sisältää 42 Moskovan pörssiin listattua osaketta kattaen vuodet 2011–2017. Tarkasteltavat strategiat on puolestaan jaoteltu viiteen osioon. Ensimmäisessä osassa tarkastelemme klassisia ja robusteja riski-tuotto -portfolioita sekä kaupankäyntikustannusten mallintamista. Havaitsemme, että odotettua tuottoa on syytä maksimoida suhteessa odotettuun vajeeseen edellyttäen samalla, että jokainen osake lisää sijoitussalkun häntäriskiä yhtä suurella osuudella. Toiseksi osoitamme, että minimivarianssiportfolioiden riskikorjattuja tuottoja voidaan parantaa robusteilla kovarianssiestimaattoreilla. Kolmanneksi toteamme robustien optimointitekniikoiden soveltuvan parhaiten konservatiivisille sijoittajille niiden tuottamien matalan volatiliteetin allokaatioiden ansiosta. Toisessa osassa hyödynnämme tilastollisia faktorimalleja korkeampien yhteismomenttien estimoinnissa ja havainnollistamme ehdotetun metodin hyödyllisyyttä riskioptimaalisten sekä odotettua hyötyä maksimoivien salkkujen rakentamisessa. Kolmannessa osassa käytämme Almgren–Chrissin viitekehystä ja asetamme odotetut tuotot suuruusjärjestykseen oletetun momentum-anomalian mukaisesti. Havaitsemme, että menetelmä tuottaa vakaita allokaatioita menestyen erityisen hyvin noususuhdanteessa. Neljännessä osassa osoitamme, että VECM- että GARCH-mallien tuottamia ennusteita voidaan hyödyntää kannattavasti niin Black–Littermanin malliin kuin kopulanäkemysten ja entropian poolaukseenkin perustuvissa optimoinneissa. Viimeisessä osassa laadimme varallisuuden suojausstrategian, joka kykenee ajoittamaan markkinoiden muutoksia ARIMA-malliin perustuvien tuottoennusteiden ansiosta. Voidaan siis todeta, että Venäjän osakemarkkinoilla on ollut mahdollista tehdä turvallisia ja tuottavia sijoituksia myös silloin kun kohtuulliset kaupankäyntikustannukset on huomioitu. Toiseksi väitämme, että markkinoiden tehottomuutta on voitu hyödyntää suunnittelemalla tilastolliseen arbitraasiin ja muihin taktiseen varojen allokointiin pohjautuvia strategioita

Machine Learning-Driven Decision Making based on Financial Time Series

L'abstract è presente nell'allegato / the abstract is in the attachmen

Financial predictions using intelligent systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This thesis presents a collection of practical techniques for analysing various market properties in order to design advanced self-evolving trading systems based on neural networks combined with a genetic algorithm optimisation approach. Nonlinear multivariate statistical models have gained increasing importance in financial time series analysis, as it is very hard to fmd statistically significant market inefficiencies using standard linear modes. Nonlinear models capture more of the underlying dynamics of these high dimensional noisy systems than traditional models, whilst at the same time making fewer restrictive assumptions about them. These adaptive trading systems can extract information about associated time varying processes that may not be readily captured by traditional models. In order to characterise the fmancial time series in terms of its dynamic nature, this research employs various methods such as fractal analysis, chaos theory and dynamical recurrence analysis. These techniques are used for evaluating whether markets are stochastic and deterministic or nonlinear and chaotic, and to discover regularities that are completely hidden in these time series and not detectable using conventional analysis. Particular emphasis is placed on examining the feasibility of prediction in fmancial time series and the analysis of extreme market events. The market's fractal structure and log-periodic oscillations, typical of periods before extreme events occur, are revealed through recurrence plots. Recurrence qualification analysis indicated a strong presence of structure, recurrence and determinism in the fmancial time series studied. Crucial fmancial time series transition periods were also detected. This research performs several tests on a large number of US and European stocks using methodologies inspired by both fundamental analysis and technical trading rules. Results from the tests show that profitable trading models utilising advanced nonlinear trading systems can be created after accounting for realistic transaction costs. The return achieved by applying the trading model to a portfolio of real price series differs significantly from that achieved by applying it to a randomly generated price series. In some cases, these models are compared against simpler alternative approaches to ensure that there is an added value in the use of these more complex models. The superior performance of multivariate nonlinear models is also demonstrated. The long-short trading strategies performed well in both bull and bear markets, as well as in a sideways market, showing a great degree of flexibility and adjustability to changing market conditions. Empirical evidence shows that information is not instantly incorporated into market pnces and supports the claim that the fmancial time series studied, for the periods analysed, are not entirely random. This research clearly shows that equity markets are partially inefficient and do not behave along lines dictated by the efficient market hypothesis

전이 엔트로피와 기계학습에 기반한 금융투자 실증 연구

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 산업공학과, 2020. 8. 장우진.Stock markets have been studied extensively as one of the crucial fields of economy. In particular, research has been actively conducted to analyze and predict the stock market based on relationships among the dynamics of stock prices and returns. In this context, transfer entropy is a non-parametric indicator in analyzing relationships between components of a system, and has a more flexible analytical ability than correlation or Granger-causality. The study of stock price prediction is also being studied from traditional linear models to the latest machine learning models, and research on the optimal asset allocation strategy based on these studies are conducted. The purpose of this dissertation is to derive ETE based network indicator with a market explanatory power for the US stock market by using effective transfer entropy, which is mainly used in econophysics and information theory. The improvement of the performance of the stock price direction prediction through various machine learning algorithms by ETE based network indicator is also analyzed. Furthermore, we apply the prediction result of the stock price through the machine learning algorithm with ETE based network indicator to optimal portfolio strategy through the Black-Litterman model to study the practical use of the investment strategy. At first, we explore that the ETE based on 3 and 6 months moving windows can be regarded as the market explanatory variable by analyzing the association between the financial crises and statistical explanatory power among the stocks. We found that 3 and 6 months moving windows ETEs increase in major financial crises, and that the sectors related to the financial crises have a statistical explanatory power to other sectors through the time-varying analysis of the ETE network indicators. Then, we discover that the prediction performance on the stock price direction can be improved when the ETE driven variable is integrated as a new feature in the logistic regression, multilayer perceptron, random forest, XGBoost, and long short-term memory network. Meanwhile, we suggest utilizing the adjusted accuracy derived from the risk-adjusted return in finance as a prediction performance measure. Notably, we confirm that the multilayer perceptron and long short-term memory network are more suitable for stock price prediction. Lastly, we examined the possibility for investors to develop an investment strategy that maximizes profits through the Black-Litterman model using ETE and machine learning. The characteristics of the inflow and outflow ETE network indicators with market explanatory power and the stock price direction prediction results using machine learning algorithms are applied to the investor's view of the Black-Literman model. The Black-Litterman portfolio, which applies the results of the stock price direction prediction using machine learning algorithms to the investor's view, provides a better return on risk than the market portfolio and market index, and the Black-Litterman portfolio with the ETE network indicator has the highest yield. The use of ETE and stock price prediction leads to improved return on investment, and improving predictive performance increases the return on investment. This dissertation is the first study on the optimal portfolio establishment strategy through the Black-Litterman model and stock price direction prediction using machine learning algorithm to apply ETE of information theory to the financial investment field.주식 시장은 경제 분야의 중요한 부분으로 광범위하게 연구되고 있다. 특히, 주식 시장의 구성 요소들인 주식 가격과 그 수익률의 관계를 예측하고 분석하는 연구는 투자자들이 최적 투자 전략을 세우기 위해 중요한 과업 중 하나이다. 이러한 맥락에서, 어떠한 시스템의 구성 요소들 간의 관계를 분석하는 데 있어 전이 엔트로피(Transfer entropy)는 비모수 지표로써 상관 관계나 그레인저-인과관계에 비해 요소 간 통계적 설명력을 확인하기에 용이하다. 주식 가격의 예측과 이를 통한 최적 자산 배분 전략에 대한 연구 또한 전통적인 선형 모델부터 최신의 머신 러닝 모델의 적용까지 다양하게 연구되고 있다. 본 학위논문의 목적은 경제물리학과 정보이론 분야에서 사용되는 효율적 전이 엔트로피(Effective transfer entropy, ETE)를 이용하여 미국 주식 시장에서 시장 구성 요소 간 발생하는 정보 흐름의 특징을 파악하여 시장의 특성을 나타낼 수 있는 시장 설명력 있는 ETE 기반의 네트워크 지표를 도출하고, 이 네트워크 지표의 사용이 다양한 머신 러닝 알고리즘을 통한 주가 방향 예측에서 성능 향상을 가져다 주는 지에 대해 연구한다. 나아가, 시장 설명력 있는 ETE 네트워크 지표의 구조적 특징과 머신 러닝 알고리즘을 통한 주가 방향 예측 결과를 투자자 관점을 고려한 최적 포트폴리오 구성 전략인 블랙-리터만 모형(Black-Litterman model)에 적용하여 결과적으로 정보 이론과 머신 러닝 기법을 이용한 실제 투자 전략 활용성에 대해 연구한다. 먼저, 미국 주식 시장의 주요 금융 위기들과 주식들 간의 통계적 설명력을 ETE를 통해 분석함으로써 3개월과 6개월 이동창을 기반으로 하는 ETE가 미국 주식 시장에 대해 설명력 있는 지표임을 확인했다. 해당 지표가 주요 금융 위기에서 그 값이 커지고, ETE 네트워크 지표의 시계열 분석을 통해 각 금융위기에서 해당 금융 위기와 관련된 섹터들이 다른 섹터들에 통계적 설명력이 있는 것을 확인했다. 다음으로, 로지스틱 회귀(Logistic regression, LR), 다층 퍼셉트론(Multilayer perceptron, MLP), 랜덤 포레스트(Random forest, RF), XGBoost(XGB) 및 Long short-term memory network(LSTM)의 5개 머신 러닝 알고리즘에 대해 ETE 네트워크 지표가 새로운 변수로 추가되었을 때 주가 방향 예측에 대한 예측 성능이 향상되는 것을 확인했다. 한편, 예측 모델의 예측 성능 평가에 대한 지표로 금융 분야에서 쓰이는 위험 조정 수익률로부터 도출한 수정 정확도 활용을 제안했고, 이 평가 지표를 이용한 분석을 통해 해당 5개 모델 중 MLP와 LSTM이 미국 주식 시장에 대한 주가 방향 예측에서 더 적합한 모델임을 확인했다. 마지막으로, 시장 설명력 있는 유입 및 유출 ETE 네트워크 지표의 특징과 머신 러닝 알고리즘을 이용한 주가 방향 예측 결과를 블랙-리터만 모형의 투자자 관점에 적용하여, 머신 러닝 알고리즘을 이용한 주가 방향 예측 결과를 투자자 관점에 적용한 블랙-리터만 포트폴리오는 시장 포트폴리오와 시장 인덱스보다 나은 위험 대비 수익률을 보이고, ETE 네트워크 지표를 적용한 블랙-리터만 포트폴리오는 가장 높은 수익률을 보임을 확인했다. ETE와 주가 방향 예측의 사용이 투자 수익률 향상으로 이어지고, 예측 성능을 향상시키면 투자 수익률도 함께 증가하는 결과를 활용하여 투자자들이 ETE와 머신 러닝을 활용한 블랙-리터만 모형을 통해 수익을 극대화 할 수 있는 투자 전략을 수립할 수 있는 가능성에 대해 확인했다. 본 학위논문은 정보 이론의 ETE를 금융 투자 분야에 적용할 수 있도록, 머신 러닝 알고리즘을 이용한 주가 방향 예측과 블랙-리터만 모형을 통한 최적 포트폴리오 구성 전략에 대한 첫 번째 연구이다.Chapter 1 Introduction 1 1.1 Background and Motivation . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.3 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . 7 Chapter 2 Literature Review 9 2.1 Analysis of transfer entropy . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Stock price prediction based on machine learning . . . . . . . . . . . 12 2.3 The Black-Litterman model . . . . . . . . . . . . . . . . . . . . . . . 17 Chapter 3 Effective transfer entropy analysis for the US market 21 3.1 Effective transfer entropy . . . . . . . . . . . . . . . . . . . . . . . . 21 3.2 Data and experiment set-ups . . . . . . . . . . . . . . . . . . . . . . 24 3.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 3.2.2 Experiment set-ups . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.1 Overall analysis of Effective transfer entropy . . . . . . . . . 31 3.3.2 Sector analysis of Effective transfer entropy . . . . . . . . . . 33 3.4 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 40 Chapter 4 Predicting the direction of US stock prices using ETE and machine learning techniques 45 4.1 Machine learning algorithms . . . . . . . . . . . . . . . . . . . . . . . 45 4.1.1 Logistic regression . . . . . . . . . . . . . . . . . . . . . . . . 45 4.1.2 Multi-layer perceptron . . . . . . . . . . . . . . . . . . . . . . 45 4.1.3 Random forest . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.1.4 Extreme gradient boosting . . . . . . . . . . . . . . . . . . . 47 4.1.5 Long short-term memory network . . . . . . . . . . . . . . . 48 4.1.6 Adjusted accuracy . . . . . . . . . . . . . . . . . . . . . . . . 49 4.2 Data and experiment set-ups . . . . . . . . . . . . . . . . . . . . . . 51 4.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2.2 Experiment set-ups . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3.1 Prediction performance in different models . . . . . . . . . . 57 4.3.2 Prediction performance in different sectors . . . . . . . . . . . 66 4.4 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 78 Chapter 5 The Black-Litterman model for ETE and machine learning 81 5.1 The Black-Litterman model . . . . . . . . . . . . . . . . . . . . . . . 81 5.2 Data and experiment set-ups . . . . . . . . . . . . . . . . . . . . . . 84 5.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 5.2.2 Experiment set-ups . . . . . . . . . . . . . . . . . . . . . . . . 85 5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.3.1 Prediction performance in different models and sectors . . . . 91 5.3.2 Portfolio performances for cumulative return . . . . . . . . . 91 5.4 Summary and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 97 Chapter 6 Conclusion 103 6.1 Contributions and Limitations . . . . . . . . . . . . . . . . . . . . . 103 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Bibliography 107 국문초록 125Docto

Investigating Market Linkages and Investor Behavior in Times of Turmoil and Uncertainty

Throughout the last decades, investigations on market linkages and investor behavior in times of turmoil and uncertainty have received the attention of researchers and financial practitioners alike. This dissertation offers five distinct research papers which contribute to the existing literature on this overarching topic. First, we provide a thorough analysis of the time-varying linkages between regional and global equity markets. Second, and in line with the notion of increasing equity market integration over time, we investigate different types of flights to quality in times of stock market turmoil. Third, we provide novel empirical evidence on the usefulness of new sources of information on investor behavior towards the measurement of financial market linkages. Fourth, building on the increasing relevance of these new sources of information, we demonstrate that different measures for online investor attention do not necessarily constitute equivalent proxies for the latent variable. Last, we contribute to the strands of financial literature dealing with the estimation of dynamic linkages between financial markets and variables in the form of time-varying correlations. More specifically, we propose a score-driven extension to the well-known dynamic conditional correlation model which provides a means to quantify the time-varying influence of news on correlation dynamics. Taking the severe impact of recent and current crisis events on financial markets into consideration, the research papers comprised in this dissertation are of uttermost importance for financial market participants

Using machine learning to forecast long-term equity price movement : an empirical study of the Finnish financial markets

Predicting equity price movement is one of the fundamental challenges in finance, and even small improvements in prediction performance can be highly profitable for investors. Long-term investment is one of the popular investment strategies that investors follow. However, evaluating which companies are going to perform well in the future is difficult. This research presents machine learning aided approach to forecast long-term price movement of the stocks listed on the Helsinki Stock Exchange. The purpose of the research is to find out which machine learning model performs the best in the Finnish financial markets and to understand what the key variables are, which have a major effect on the prediction accuracy of the models. The research is also testing whether the macroeconomic variables of Finland increase the accuracy of the machine learning models when forecasting long-term equity price movement. The following machine learning models are used in the research: logistic regression, support vector machine, decision tree, random forest, and k-nearest neighbors. This research produced a number of key findings: the results from the models indicated that the best performance was achieved by the random forest model, which obtained classification accuracy of 65.3% and F1 score of 60.8%; the random forest model is able to give over 60% chance for an investor to pick a stock, which will have a 10% or higher return over the period of one year; the macroeconomic variables increased the prediction performance of every machine learning model used in the research. The main conclusions drawn from this research are that the macroeconomic variables can provide new information, which is not explained by only using financial ratios in the models. Also, the equity prices in the Finnish financial markets are not equally random, meaning that they do not always follow a random walk process. Therefore, this research argues that the Finnish financial market is not highly efficient, thus stock prices are on some level predictable. These findings contribute to the financial theory of market efficiency