Recognizing and forecasting the sign of financial local trends using hidden Markov models

The problem of forecasting financial time series has received great attention in the past, from both Econometrics and Pattern Recognition researchers. In this context, most of the efforts were spent to represent and model the volatility of the financial indicators in long time series. In this paper a different problem is faced, the prediction of increases and decreases in short (local) financial trends. This problem, poorly considered by the researchers, needs specific models, able to capture the movement in the short time and the asymmetries between increase and decrease periods. The methodology presented in this paper explicitly considers both aspects, encoding the financial returns in binary values (representing the signs of the returns), which are subsequently modelled using two separate Hidden Markov models, one for increases and one for decreases, respectively. The approach has been tested with different experiments with the Dow Jones index and other shares of the same market of different risk, with encouraging results

A Classifying Procedure for Signaling Turning Points

A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead o maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimation for different forecasting horizons. The inference aspect is emphasized by including a penalty for a wrong decision in the cost function. The method is tested by forecasting turning points in the Swedish and US economies, using leading data. Clear and early turning point signals are obtained, contrasting favourable with earlier HMM studies. Some theoretical arguments for this are given.Business Cycle; Feature Extraction; Hidden Markov Switching-Regime Model; Leading Indicator; Probability Forecast.

Essays on Financial Applications of Nonlinear Models

In this thesis, we examine the relationship between news and the stock market. Further, we explore methods and build new nonlinear models for forecasting stock price movement and portfolio optimization based on past stock prices and on one type of big data, news items, which are obtained through the RavenPack News Analytics Global Equities editions. The thesis consists of three essays. In Essay 1, we investigate the relationship between news items and stock prices using the artificial neural network (ANN) model. First, we use Granger causality to ascertain how news items affect stock prices. The results show that news volume is not the Granger cause of stock price change; rather, news sentiment is. Second, we test the semi–strong form efficient market hypothesis, whereas most existing research testing efficient market hypothesis focuses on the weak–form version. Our ANN strategies consistently outperform the passive buy–and–hold strategy and this finding is apparently at odds with the notion of the efficient market hypothesis. Finally, using news sentiment analytics from RavenPack Dow Jones News Analytics, we show positive profitability with out–of–sample prediction using the proposed ANN strategies for Google Inc. (NASDAQ: GOOG). In Essay 2, we expand the utility of the information from news volume and news sentiments to encompass portfolio diversification. For the Dow Jones Industrial Average (DJIA) components, we assign different weights to build portfolios according to their weekly news volumes or news sentiments. Our results show that news volume contributes to portfolio variance both in–sample and out–of–sample: positive news sentiment contributes to the portfolio return in–sample, while negative contributes to the portfolio return out–of–sample, which is a consequence of investors overreacting to the news sentiment. Further, we propose a novel approach to portfolio diversification using the k–Nearest Neighbors (kNN) algorithm based on the idea that news sentiment correlates with stock returns. Out–of–sample results indicate that such strategy dominates the benchmark DJIA index portfolio. In Essay 3, we propose a new model called the Combined Markov and Hidden Markov Model (CMHMM), in which observation is affected by a Markov model and an HMM (Hidden Markov Model) model. The three fundamental questions of the CMHMM are discussed. Further, the application of the CMHMM, in which the news sentiment is one observation and the stock return is the other, is discussed. The empirical results of the trading strategy based on the CMHMM show the potential applications of the proposed model in finance. This thesis contributes to the literature in a number of ways. First, it extends the literature on financial applications of nonlinear models. We explore the applications of the ANNs and kNN in the financial market. Besides, the proposed new CMHMM model adheres to the nature of the stock market and has better potential prediction ability. Second, the empirical results from this dissertation contribute to the understanding of the relationship between news and the stock market. For instance, our research found that news volume contributes to the portfolio return and that investors overreact to news sentiment—a phenomenon that has been discussed by other scholars from different angles

Markovian model for forecasting financial time series

The study aims to create a Markovian model for forecasting financial time series and measure its effectiveness on stock prices. In the study, the new forecaster was inspired by several machine learning techniques and included statistical approaches and conditional probabilities. Namely, Markov Chains and Hidden Markov Chains are the main inspiration for machine learning techniques. To be able to process time series with Markov Chains like algorithm, new transformation developed with the usage of daily stock prices. Thirteen years of daily stock prices have been used for the data feed. For measuring the effectiveness of a new predictor, the obtaıned results are compared with conventional methods such as ARIMA, linear regression, decision tree regression and support vector regression predictions. The comparisons presented are based on Mean Absolute Percentage Error (MAPE) and Root Mean Square Error ( RMSE). According to the achieved results, the new predictor performs better than decision tree regression, and ARIMA performs best among them.O estudo tem como objectivo criar um modelo markoviano para a previsão de séries temporais e medir a eficácia deste nas previsões de preços das ações. No estudo, o novo previsor foi inspirado em várias técnicas de aprendizagem de máquinas e incluiu abordagens estatísticas e probabilidades condicionais. Ou seja, as cadeias de Markov são a principal inspiração das técnicas para a aprendizagem das máquinas. Para ser capaz de processar séries temporais com algorítmo do tipo Cadeias de Markov, a nova técnica é desenvolvida com base em preços diários e ações. Foram considerados treze anos de preços diários de ações para teste dos modelos. Para medir a eficácia do novo previsor, foram obtidos resultados comparados com métodos convencionais, como os modelos ARIMA, a regressão linear, a regressão a partir da árvore de decisão. Esta comparação foi efetuada com base no Erro Absoluto Médio Percentual (MAPE) e na Raiz do Erro Quadrático Médio (RMSE). De acordo com os resultados obtidos, o novo previsor tem melhor desempenho do que a regressão da árvore de decisão, e o ARIMA tem o melhor desempenho entre eles

Hidden Markov Models for Stock Market Prediction

The stock market presents a challenging environment for accurately predicting future stock prices due to its intricate and ever-changing nature. However, the utilization of advanced methodologies can significantly enhance the precision of stock price predictions. One such method is Hidden Markov Models (HMMs). HMMs are statistical models that can be used to model the behavior of a partially observable system, making them suitable for modeling stock prices based on historical data. Accurate stock price predictions can help traders make better investment decisions, leading to increased profits. In this article, we trained and tested a Hidden Markov Model for the purpose of predicting a stock closing price based on its opening price and the preceding day's prices. The model's performance has been evaluated using two indicators: Mean Average Prediction Error (MAPE), which specifies the average accuracy of our model, and Directional Prediction Accuracy (DPA), a newly introduced indicator that accounts for the number of fractional change predictions that are correct in sign

Modelling and trading the Greek stock market with gene expression and genetic programing algorithms

This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter

Predicting market direction with hidden Markov models

This paper develops the model of Bicego, Grosso, and Otranto (2008) and applies Hidden Markov Models to predict market direction. The paper draws an analogy between financial markets and speech recognition, seeking inspiration from the latter to solve common issues in quantitative investing. Whereas previous works focus mostly on very complex modifications of the original hidden markov model algorithm, the current paper provides an innovative methodology by drawing inspiration from thoroughly tested, yet simple, speech recognition methodologies. By grouping returns into sequences, Hidden Markov Models can then predict market direction the same way they are used to identify phonemes in speech recognition. The model proves highly successful in identifying market direction but fails to consistently identify whether a trend is in place. All in all, the current paper seeks to bridge the gap between speech recognition and quantitative finance and, even though the model is not fully successful, several refinements are suggested and the room for improvement is significant.UNL - NSB