scholarly journals Strategies of portfolio investment with estimates of bull and bear markets

2021 ◽  
Vol 19 (4) ◽  
pp. 160-185
Author(s):  
Pedro L. Valls Pereira ◽  
André Barbosa De Oliveira
Keyword(s):  
Stock Market ◽  
Financial Market ◽  
Multivariate Model ◽  
Portfolio Investment ◽  
Investment Strategies ◽  
Market Index ◽  
Return Behavior ◽  
Bear Markets ◽  
Market Status ◽  
Bull And Bear Markets

The financial market has non-linear patterns, with different return behavior in bull versus bear markets. This article uses multivariate model estimates to study portfolios in changing conditions, and develops investment strategies for portfolios in light of uncertainty about the bear or bull status of the stock market. Portfolios were optimized for the main stocks listed on the Brazilian market index Ibovespa. The portfolios proposed with estimates of changing market status outperformed others over the analyzed period, with rebalancing adjustments made either weekly or monthly.

scholarly journals An Improved Probabilistic Neural Network Model for Directional Prediction of a Stock Market Index

2019 ◽  
Vol 9 (24) ◽  
pp. 5334 ◽  
Author(s):  
Vasana Chandrasekara ◽  
Chandima Tilakaratne ◽  
Musa Mammadov
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Financial Market ◽  
Joint Distribution ◽  
Probabilistic Neural Network ◽  
Stock Market Index ◽  
Multivariate Distributions ◽  
Market Index ◽  
Input Variables ◽  
T Distribution

Financial market prediction attracts immense interest among researchers nowadays due to rapid increase in the investments of financial markets in the last few decades. The stock market is one of the leading financial markets due to importance and interest of many stakeholders. With the development of machine learning techniques, the financial industry thrived with the enhancement of the forecasting ability. Probabilistic neural network (PNN) is a promising machine learning technique which can be used to forecast financial markets with a higher accuracy. A major limitation of PNN is the assumption of Gaussian distribution as the distribution of input variables which is violated with respect to financial data. The main objective of this study is to improve the standard PNN by incorporating a proper multivariate distribution as the joint distribution of input variables and addressing the multi-class imbalanced problem persisting in the directional prediction of the stock market. This model building process is illustrated and tested with daily close prices of three stock market indices: AORD, GSPC and ASPI and related financial market indices. Results proved that scaled t distribution with location, scale and shape parameters can be used as more suitable distribution for financial return series. Global optimization methods are more appropriate to estimate better parameters of multivariate distributions. The global optimization technique used in this study is capable of estimating parameters with considerably high dimensional multivariate distributions. The proposed PNN model, which considers multivariate scaled t distribution as the joint distribution of input variables, exhibits better performance than the standard PNN model. The ensemble technique: multi-class undersampling based bagging (MCUB) was introduced to handle class imbalanced problem in PNNs is capable enough to resolve multi-class imbalanced problem persisting in both standard and proposed PNNs. Final model proposed in the study with proposed PNN and proposed MCUB technique is competent in forecasting the direction of a given stock market index with higher accuracy, which helps stakeholders of stock markets make accurate decisions.

Intraday Stock Market Behavior After Shocks: The Importance of Bull and Bear Markets in Spain

2014 ◽  
Vol 15 (2) ◽  
pp. 144-159 ◽  
Author(s):  
Jose Luis Miralles-Marcelo ◽  
Jose Luis Miralles-Quiros ◽  
Maria del Mar Miralles-Quiros
Keyword(s):  
Stock Market ◽  
Market Behavior ◽  
Bear Markets ◽  
Bull And Bear Markets

scholarly journals Sticky Stock Market Analysts

2021 ◽  
Vol 14 (12) ◽  
pp. 593
Author(s):  
Ibrahim Filiz ◽  
Jan René Judek ◽  
Marco Lorenz ◽  
Markus Spiwoks
Keyword(s):  
Stock Market ◽  
Investment Strategies ◽  
Stock Market Index ◽  
Market Index ◽  
German Stock ◽  
Long Term Trends ◽  
German Stock Market ◽  
Better Than

Technological progress in recent years has made new methods available for making forecasts in a variety of areas. We examine the success of ex-ante stock market forecasts of three major stock market indices, i.e., the German Stock Market Index (DAX), the Dow Jones Industrial Index (DJI), and the Euro Stoxx 50 (SX5E). We test whether the forecasts prove true when they reach their effective dates and are therefore suitable for active investment strategies. We revive the thoughts of the American sociologist William Fielding Ogburn, who argues that forecasters consistently underestimate the variability of the future. In addition, we draw on some contemporary measures of forecast quality (prediction-realization diagram, test of unbiasedness, and Diebold–Mariano test). We reveal that (a) unusual events are underrepresented in the forecasts, (b) the dispersion of the forecasts lags behind that of the actual events, (c) the slope of the regression lines in the prediction-realization diagram is <1, (d) the forecasts are highly biased, and (e) the quality of the forecasts is not significantly better than that of naïve forecasts. The overall behavior of the forecasters can be described as “sticky” because their forecasts adhere too strongly to long-term trends in the indices and are thus characterized by conservatism.

Optimal Goals-Based Investment Strategies for Switching Between Bull and Bear Markets

2021 ◽  
pp. jwm.2021.1.161
Author(s):  
Sanjiv R. Das ◽  
Daniel Ostrov ◽  
Aviva Casanova ◽  
Anand Radhakrishnan ◽  
Deep Srivastav
Keyword(s):  
Investment Strategies ◽  
Bear Markets ◽  
Bull And Bear Markets

Optimal Goals-Based Investment Strategies For Switching Between Bull and Bear Markets

2021 ◽  
Author(s):  
Sanjiv Ranjan Das ◽  
Daniel N Ostrov ◽  
Aviva Casanova ◽  
Anand Radhakrishnan ◽  
Deep Srivastav
Keyword(s):  
Investment Strategies ◽  
Bear Markets ◽  
Bull And Bear Markets
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