A Study On The Simple Random Walk

An important class of Markov chain problems is the random walk problems. In a random walk the state of the Markov chain are the integers and the jumps of the chain from stat

Are Stock Prices a Random Walk? An Empirical Evidence of Asian Stock Markets

Investigating if the market is efficient is an old issue as market efficiency is imperative for channeling investments to best-valued projects and its importance endures. There is contradictory evidence in the literature provided by empirical researches. The primary purpose of this research has been to find out whether share prices are a random walk process by applying multiple unit root tests, Runs Test and newly developed State Space Model. The empirical findings of the study provide sufficient evidence that the stock prices of KSE 100 Index, S &amp; P BSE 500 Index, and CSE All Share Index is not a random walk process and are thus weak form inefficient hypothesis. In this study, the concept of the random walk is examined considering only the stock markets while bypassing the other asset markets. This research supply exciting facts about independent samples from Pakistan, India, and Bangladesh and complement the existing literature on emerging markets.DOI: 10.15408/etk.v17i2.7102</p

Developing a hybrid hidden MARKOV model using fusion of ARMA model and artificial neural network for crude oil price forecasting

Crude oil price forecasting is an important component of sustainable development of many countries as crude oil is an unavoidable product that exist on earth. Crude oil price forecasting plays a very vital role in economic development of many countries in the world today. Any fluctuation in crude oil price tremendously affects many economies in terms of budget and expenditure. In view of this, it is of great concern by economists and financial analysts to forecast such a vital commodity. However, Hidden Markov Model, ARMA Model and Artificial Neural Network has many drawbacks in forecasting such as linear limitations of ARMA model which is in contrast to the financial time series which are often nonlinear, ANN is very weak in terms of out-sample forecast and it has very tedious process of implementation, HMM is very weak in an in-sample forecast and has issue of a large number of unstructured parameters. In view of this drawbacks of these three models (ANN, ARMA and HMM), we developed an efficient Hybrid Hidden Markov Model using fusion of ARMA Model and Artificial Neural Network for crude oil price forecasting, MATLAB was employed to develop the four models (Hybrid HMM, HMM, ARMA and ANN). The models were evaluated using three different evaluation techniques which are Mean Absolute Percentage Error (MAPE), Absolute Error (AE) and Root Mean Square Error (RMSE). The findings showed that Hybrid Hidden Markov Model was found to provide more accurate crude oil price forecast than the other three models in which. The results of this study indicate that Hybrid Hidden Markov Model using fusion of ARMA and ANN is a potentially promising model for crude oil price forecasting

Comparative study on retail sales forecasting between single and combination methods

In today’s competitive global economy, businesses must adjust themselves constantly to ever-changing markets. Therefore, predicting future events in the marketplace is crucial to the maintenance of successful business activities. In this study, sales forecasts for a global furniture retailer operating in Turkey were made using state space models, ARIMA and ARFIMA models, neural networks, and Adaptive Network-based Fuzzy Inference System (ANFIS). Also, the forecasting performances of some widely used combining methods were evaluated by comparison with the weekly sales data for ten products. According to the best of our knowledge, this study is the first time that the recently developed state space models, also called ETS (Error-Trend-Seasonal) models, and the ANFIS model have been tested within combining methods for forecasting retail sales. Analysis of the results of the single models in isolation indicated that none of them outperformed all the others across all the time series investigated. However, the empirical results suggested that most of the combined forecasts examined could achieve statistically significant increases in forecasting accuracy compared with individual models and with the forecasts generated by the company’s current system

Neuroverkkopohjaisen sijoitusstrategian hyödyntäminen indeksiosuusrahaston ennustamisessa

Tässä tutkimuksessa selvitetään, voiko koulutettua keinotekoista neuroverkkoa hyödyntää indeksiosuusrahaston ennustamisessa. Tutkimuksen teoriataustana käytetään tehokkaiden markkinoiden hypoteesia. Markkinoiden toimiessa tehokkaasti kaikki julkinen ja yrityksen arvon kannalta oleellinen uusi tieto heijastuu välittömästi ja täysimääräisesti arvopapereiden hintoihin. Tehokkailla markkinoilla yksittäinen sijoittaja ei voi saavuttaa säännöllisesti markkinoiden riskikorjattua tuottoa ylittävää ylituottoa. Tutkimuksen teoriaosuus perustuu rahoituksen taloustieteeseen ja menetelmäosuus koneoppimiseen. Tutkimusmenetelmänä käytetään keinotekoista neuroverkkoa, joka koulutetaan oppimaan rahoitusmarkkinoilta saatavien syötteiden avulla hinnan muodostumisen mekanismia. Koulutuksessa opittua mekanismia hyödynnetään seuraavan päivän indeksiosuusrahaston hintojen ennustamisessa. Oppimisalgoritmina käytetään Levenberg-Marquardt algoritmia. Algoritmin ennusteita muokataan erilaisten sijoitusstrategioiden avulla tarkempien ennusteiden saavuttamiseksi. Tämän tutkimuksen kohteena ovat pörssinoteeratun iShares Core S&P 500 -rahaston päätöskurssihinnat vuodesta 2005 vuoden 2015 loppuun. Aineistona käytetään S&P 500 -indeksistä johdettua historiallista aikasarja-aineistoa 1950-luvulta lähtien. Tutkimuksen mukaan neuroverkkoa voidaan hyödyntää iShares Core S&P 500 -indeksiosuusrahaston tuottojen suunnan ennustamisessa. Transaktiokustannusten ollessa alhaiset neuroverkosta johdetuilla ennusteilla saavutetaan hyviä tuottoja, muttei kuitenkaan markkinoiden riskikorjattua tuottoa ylittävää ylituottoa

Contribution to Financial Modeling and Financial Forecasting

This thesis consists of three chapters. Each chapter is independent research that is conducted during my study. This research is concentrated on financial time series modeling and forecasting. On first chapter, the research aims to prove that any abnormal behavior in debt level is a signal of future unexpected return for firms that is listed in indexes in this study, hence it is a signal to buy. In order to prove this theory multiple indexes from around the world were taken into consideration. This behavior is consistent in most of indexes around the word. The second chapter investigate the effect of United State president speech on value of United State Currency in Foreign Exchange Rate market. In this analysis it is shown that during the time the president is delivering a speech there is distinctive changes in USD value and volatility in global markets. This chapter implies that this effect cannot be captured by linear models, and the impact of the presidential speech is short term. Finally, the third chapter which is the major research of this thesis, suggest two new methods that potentially enhance the financial time series forecasting. Firstly, the new ARMA-RNN model is presented. The suggested model is inheriting the process of Autoregressive Moving Average model which is extensively studied, and train a recurrent neural network based on it to benefit from unique ability of ARMA model as well as strength and nonlinearity of artificial neural network. Secondly the research investigates the use of different frequency of data for input layer to predict the same data on output layer. In other words, artificial neural networks are trained on higher frequency data to predict lower frequency. Finally, both stated method is combined to achieve more superior predictive model