scholarly journals Stock market trend prediction using regression errors

2021 ◽  
Author(s):  
Omair Sandhu
Keyword(s):  
Stock Market ◽  
Factor Model ◽  
Regression Line ◽  
Stock Exchanges ◽  
Forecasting Model ◽  
Trend Prediction ◽  
Monthly Data ◽  
Trend Forecasting ◽  
Direction Of Movement ◽  
Multiple Assets

Stock exchanges are one of the major areas of investment because of the possibility of high returns and big winners. They are affected by a variety of factors making it difficult to get consistent returns and accurate predictions when using systematic forecasting techniques. We consider a portfolio formation problem by comparison of the trend strengths of multiple assets. The trend strength determined by the slope and errors from the regression line provides a useful method for crosssectional comparison of stocks. We use weekly and monthly data from 1965 to 2018 from the CRSP US Stocks Database to test the performance of these factors when used to predict the direction of movement for an asset in the future. We investigate the feasibility of this two factor model and various methods of combination to determine the optimal stock trend forecasting model.

scholarly journals Stock market trend prediction using regression errors

2021 ◽  
Author(s):  
Omair Sandhu
Keyword(s):  
Stock Market ◽  
Factor Model ◽  
Regression Line ◽  
Stock Exchanges ◽  
Forecasting Model ◽  
Trend Prediction ◽  
Monthly Data ◽  
Trend Forecasting ◽  
Direction Of Movement ◽  
Multiple Assets

Stock exchanges are one of the major areas of investment because of the possibility of high returns and big winners. They are affected by a variety of factors making it difficult to get consistent returns and accurate predictions when using systematic forecasting techniques. We consider a portfolio formation problem by comparison of the trend strengths of multiple assets. The trend strength determined by the slope and errors from the regression line provides a useful method for crosssectional comparison of stocks. We use weekly and monthly data from 1965 to 2018 from the CRSP US Stocks Database to test the performance of these factors when used to predict the direction of movement for an asset in the future. We investigate the feasibility of this two factor model and various methods of combination to determine the optimal stock trend forecasting model.

Forecasting Russian Stock Market Trends

2003 ◽  
pp. 81-94 ◽  
Author(s):  
A. Rozhkov
Keyword(s):  
Stock Market ◽  
Economic Indicators ◽  
Forecasting Model ◽  
Reverse Trend ◽  
Trend Forecasting ◽  
Market Trends ◽  
Russian Stock Market

The article is devoted to investigating methods for forecasting long-term Russian stock market trends. The purpose of research is creation of the forecasting model capable of forming a reverse trend signal in the stock market. The index of trend forecasting constructed in the article includes different economic indicators and thus has high forecasting ability.

scholarly journals Testing the overreaction hypothesis in the mexican stock market

2019 ◽  
Vol 65 (1) ◽  
pp. 153
Author(s):  
Jaime González Maiz Jiménez ◽  
Edgar Ortiz Calisto
Keyword(s):  
Stock Market ◽  
Factor Model ◽  
Monthly Data ◽  
Car Model ◽  
Overreaction Hypothesis ◽  
Fama And French ◽  
Capm Model ◽  
Three Factor ◽  
Loser Portfolio

<p>The objective of this work is to test the overreaction hypothesis in the Mexican Stock Market for the period of 2002-2015, using monthly data and applying the Cumulative Average Residuals (CAR) methodology via the CAPM model and the three-factor model proposed by Fama and French. The CAR model is applied to test how winner and loser portfolios perform during the period under analysis. Overall, the evidence shows that average CAR for the loser portfolio is 0.706%, whereas CAR for the winner portfolio is 0.364%, and that are statistically different; nevertheless, both portfolios are co-integrated. This research contributes to the financial literature identifying overreaction in the Mexican Stock Market during the period examined.</p>

Pricing models of shares of Russian companies and their practical application

2019 ◽  
pp. 48-76 ◽  
Author(s):  
Alexander E. Abramov ◽  
Alexander D. Radygin ◽  
Maria I. Chernova
Keyword(s):  
Stock Market ◽  
Institutional Investors ◽  
Factor Model ◽  
Investment Strategies ◽  
Pricing Models ◽  
Foreign Portfolio Investment ◽  
Market Portfolio ◽  
Stock Market Performance ◽  
Long Time ◽  
Russian Stock Market

The article analyzes the problems of applying stock pricing models in the Russian stock market. The novelty of the study lies in the peculiarities of the methodology used and the substantive conclusions on the specifics of the influence of fundamental factors on the pricing of shares of Russian companies. The study was conducted using its own 5-factor basic pricing model based on a sample of the most complete number of issues of shares of Russian issuers and a long time horizon, from 1997 to 2017. The market portfolio was the widest for a set of issuers. We consider the factor model as a kind of universal indicator of the efficiency of the stock market performance of its functions. The article confirms the significance of factors of a broad market portfolio, size, liquidity and, in part, momentum (inertia). However, starting from 2011, the significance of factors began to decrease as the qualitative characteristics of the stock market deteriorated due to the outflow of foreign portfolio investment, combined with the low level of development of domestic institutional investors. Also identified is the cyclical nature of the actions of company size and liquidity factors. Their ability to generate additional income on shares rises mainly at the stage of the fall of the stock market. The results of the study suggest that as domestic institutional investors develop on the Russian stock market, factor investment strategies can be used as a tool to increase the return on investor portfolios.

A novel cryptocurrency price trend forecasting model based on LightGBM

2020 ◽  
Vol 32 ◽  
pp. 101084 ◽  
Author(s):  
Xiaolei Sun ◽  
Mingxi Liu ◽  
Zeqian Sima
Keyword(s):  
Forecasting Model ◽  
Price Trend ◽  
Model Based ◽  
Trend Forecasting

scholarly journals Diversification opportunities in European stock markets and their impact on textile industry development based on a financial education approach

2021 ◽  
Vol 72 (05) ◽  
pp. 528-537
Author(s):  
CRISTI SPULBĂR ◽  
RAMONA BIRĂU ◽  
VICTOR OLUWI ◽  
ABDULLAH EJAZ ◽  
TIBERIU HORAȚIU GORUN ◽  
...  
Keyword(s):  
Stock Market ◽  
Textile Industry ◽  
Stock Exchange ◽  
Financial Education ◽  
Sustainable Growth ◽  
Stock Exchanges ◽  
Portfolio Risk ◽  
Stock Indices ◽  
High Investment ◽  
European Stock Markets

This research study explores the diversification opportunity among 18 European stock market indices for the sample period from January 2001 to December 2019. However, financial education plays an important role in the development of the textile industry, considering the dynamics of the companies listed on the European stock exchanges. The correlation matrix, pairwise cointegration and Johansen cointegration reveal that selected 18 European stock market indices do not reduces the portfolio risk because exhibit higher positive correlation among them, and their movement pulsed in tandem. Potential investors are attracted by high investment opportunities in order to maximize their return based on portfolio diversification. Financial education can effectively contribute to the sustainable growth of the textile industry in Europe. This empirical research provides an integrated perspective on the long-term evolution of certain major European stock exchange indices. The findings have significant implications for investors interested in selecting these European stock indices in order to diversify their portfolio risk. Our study also imply that selected stock indices have been strongly affected by similar political and financial belies across Europe thus, eliminating the possibility of portfolio risk diversification.

scholarly journals Peramalan Data Inflow dan Outflow Uang Kartal Bank Indonesia Provinsi DKI Jakarta Menggunakan Model ARIMAX dan SARIMAX

2021 ◽  
Vol 4 (2) ◽  
pp. 87
Author(s):  
Atika Amalia ◽  
Etik Zukhronah ◽  
Sri Subanti
Keyword(s):  
Time Series ◽  
Crucial Role ◽  
Forecasting Model ◽  
Gregorian Calendar ◽  
Monthly Data ◽  
Sample Data ◽  
The Difference

<p><strong>A</strong><strong>bstract</strong><strong>.</strong> DKI Jakarta Province plays a crucial role as the center of government and economy in Indonesia. The description of currency inflows and outflows is highly required before Bank Indonesia formulates the appropriate policies to control the circulation of money. The monthly data of currency inflow and outflow of Bank Indonesia of DKI Jakarta show a significant increase in each year particularly before, during, and after Eid al-Fitr. The determination of Eid al-Fitr does not follow the Gregorian calendar but based on the Islamic calendar. The difference in the use of the Gregorian and Islamic calendars in a time series causes a calendar variation. Thus, the determination of Eid al-Fitr in the Gregorian calendar changes as it goes forward eleven days each year or one month every three years. This study aims to obtain the best model and forecast currency inflows and outflows of Bank Indonesia DKI Jakarta using the ARIMAX and SARIMAX models. The study used in-sample data from January 2009 to December 2018 and out-sample data from January to October 2019. The best model was selected based on the smallest out-sample MAPE value. The result showed that the best forecasting model of inflow was ARIMAX (1,0,1). Meanwhile, the best forecasting model for outflow was SARIMAX (2,0,1)(0,0,1)<sup>12</sup>.</p><p><strong>Keywords: </strong>ARIMAX, calendar variation, forecasting, SARIMAX</p>

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