Optimization of Stock Portfolios Using Goal Programming Based on the Kalman-Filter Method

Long-term stock investment development is carried out by means of portfolio optimization. Selection of stocks for portfolios is not only based on high-value stock prices but also takes into account their fluctuations. Estimation of future stock price fluctuations has an indirect impact on future portfolio formation. This research has implemented the Kalman filter method to obtain the best estimation results from various stock prices with a high degree of accuracy. The results are then used to form a stock portfolio on the basis of Goal Programming. This study has compared the optimization results with the real value of stock prices. The results obtained, Kalman filter-based Goal Programming is more effective for predicting future portfolios compared to the Goal Programming method with a return difference of Rp. 178,039,848. This suggests that optimization with the Kalman Filter-based Objective Programming can be used as a tool to determine future stock portfolios

Analysis and classification of technical analysis indicators by support vector machines

[EN] The search for models which can accurately forecast the market trend has developed over the past decades. Technical indicators and oscillators are the most usually employed inputs in the prediction models. These inputs basically rely on prices and the evolution of the index itself, which may cause some problems like multicolinearity and autocorrelation, in the case of linear models, or overoptimization and noise, in the case of neural networks. This paper proposes filtering the inputs to be employed in the models. To this end, their impact on the forecast will be analysed. A support vector machine will be used to this end, in order to characterize both inputs (indicators and oscillators) and output (market trend). Doing this, it can be assessed whether the relationship between the different inputs and the market trend offers relevant information regarding the contribution of the inputs in the prediction process and whether this contribution remains constant over time. Those inputs will be selected, which obtain more stable forecasts in order to obtain more consistent predictions.[ES] La búsqueda de modelos para la predicción de la tendencia de los índices bursátiles se ha desarrollado en las últimas décadas. Los indicadores y osciladores técnicos son los inputs más utilizados en todos los modelos. Éstos se basan fundamentalmente en los precios y dirección del propio índice. Esto puede provocar ciertos problemas en las estimaciones y procesos de aprendizajes de los diferentes modelos, como multicolinealidad y autocorrelación para el caso de modelos lineales y problemas de sobreoptimización y ruido en otros casos como en las redes neuronales. Se plantea filtrar los diferentes indicadores y osciladores técnicos a utilizar en los diferentes modelos. Para ellos, se va a analizar el impacto que tienen éstos en el proceso de predicción de la tendencia de un índice bursátil. El modelo utilizado es la support vector machine que permite encontrar las características tanto de los inputs (indicadores y osciladores) como del output (la tendencia del índice). Este mapeo de la relación de los indicadores y la tendencia ofrece información relevante sobre si dicha contribución a su predicción es estable en el tiempo. Por tanto, se seleccionarán aquellos inputs cuyas características estabilicen las predicciones en los modelos. Así pues, se deben descartar aquellos indicadores irregulares, aunque puntualmente puedan alcanzar ratios de acierto algo más elevadas que los más estables. Este proceso provocará obtener predicciones de la tendencia más consistentes.Oliver-Muncharaz, J. (2018). Análisis y clasificación de indicadores técnicos mediante support vector machine. Finance, Markets and Valuation. 4(1):81-93. http://hdl.handle.net/10251/122883S81934

Exchange rates and stock markets in emerging economies: new evidence using the Quantile-on-Quantile approach

This study aims to reconsider the relationship between exchange rate and stock market returns for selected emerging countries. The quantile-on-quantile approach is employed to present an inclusive and detailed image of the association between the variables under investigation. This approach can reveal the heterogeneous and the varying relationship between the variables at different quantiles. The estimation outcome demonstrates that the examined countries’ stock market performances are not affected by the exchange rate changes unless certain market conditions are established. The empirical results suggest that the exchange rate flexibility has a crucial role in determining the market returns depending on the bearish or bullish conditions. Considering the asymmetric nature of the relationship between the exchange rate and the stock market, presented results can aid governmental authorities and investors to design dynamic economic policies and investment strategies

Generating Buy/Sell Signals for an Equity Share Using Machine Learning

This study proposes a novel model for predicting 5 days’ ahead share price direction of GARAN (Garanti Bankasi A.Ş.), an equity share that is the top traded stock in BIST100, Istanbul Stock Exchange -Turkey. The first model includes global macroeconomic indicators as well as local inputs whereas the second model is focused more on local inputs. The performances of the two models are tested using Support Vector Machines (SVM), Neural Network with Back-Propagation (BPN), and Decision Tree (DT) algorithms. Though BPN and SVM have previously been used to predict BIST100 Index movement, DT has not been utilized before with this purpose. Forecasting is carried out tested for a time span of about 6 months on a rolling horizon basis, that is, algorithms are re-run weekly with updated data to generate daily buy/sell signals for the next week. A simple trading strategy is implemented based on buy/sell signals to calculate the rate of return on investment during the testing period. The results illustrate that DT having 80% prediction accuracy outperforms BPN and SVM that achieve 60% accuracy. Consequently, DT achieves a higher rate of return

Data analytic approach for manipulation detection in stock market

The term “price manipulation” is used to describe the actions of “rogue” traders who employ carefully designed trading tactics to incur equity prices up or down to make profit. Such activities damage the proper functioning, integrity, and stability of the financial markets. In response to that, the regulators proposed new regulatory guidance to prohibit such activities on the financial markets. However, due to the lack of existing research and the implementation complexity, the application of those regulatory guidance, i.e. MiFID II in EU, is postponed to 2018. The existing studies exploring this issue either focus on empirical analysis of such cases, or propose detection models based on certain assumptions. The effective methods, based on analysing trading behaviour data, are not yet studied. This paper seeks to address that gap, and provides two data analytics based models. The first one, static model, detects manipulative behaviours through identifying abnormal patterns of trading activities. The activities are represented by transformed limit orders, in which the transformation method is proposed for partially reducing the non-stationarity nature of the financial data. The second one is hidden Markov model based dynamic model, which identifies the sequential and contextual changes in trading behaviours. Both models are evaluated using real stock tick data, which demonstrate their effectiveness on identifying a range of price manipulation scenarios, and outperforming the selected benchmarks. Thus, both models are shown to make a substantial contribution to the literature, and to offer a practical and effective approach to the identification of market manipulation

Do the FAMA and FRENCH Five-Factor model forecast well using ANN?

Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with the nonlinear nature of stock returns. Contrarily, Artificial Neural Networks (ANN) is a flexible machine learning tool which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and French five-factor model. We construct ANN’s based on the composite factors of the FF5F model to predict portfolio returns in two stages; in stage one, the study identifies the best-fit combination of training, testing, and validation along with the number of neurons full sample period. In stage two, the study uses this best combination to forecast the model under 48-months rolling window analysis. In-sample and out-sample comparisons, regression, and goodness of fit test and actual and predicted values of the stock returns of our ANN model reveal that the proposed model accurately predicts the one-month ahead returns. Our findings reinforce the investment concept that the markets compensate the high-risk portfolios more than mid and low beta portfolios and the methodology will significantly improve the return on investment of the investors