Top performing stocks recommendation strategy for portfolio
@article{Gupta2019TopPS, title={Top performing stocks recommendation strategy for portfolio}, author={Kartikay Gupta and Niladri Chatterjee}, journal={ArXiv}, year={2019}, volume={abs/1901.11013} }
Stock return forecasting is of utmost importance in the business world. This has been the favourite topic of research for many academicians since decades. Recently, regularization techniques have reported to tremendously increase the forecast accuracy of the simple regression model. Still, this model cannot incorporate the effect of things like a major natural disaster, large foreign influence, etc. in its prediction. Such things affect the whole stock market and are very unpredictable. Thus…
References
SHOWING 1-10 OF 18 REFERENCES
A stock market portfolio recommender system based on association rule mining
- Computer ScienceAppl. Soft Comput.
- 2013
A Comprehensive Look at the Empirical Performance of Equity Premium Prediction II
- Economics, BusinessSSRN Electronic Journal
- 2021
Economists have suggested a whole range of variables that predict the equity premium: dividend price ratios, dividend yields, earnings-price ratios, dividend payout ratios, corporate or net issuing…
Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?
- Economics
- 2008
Goyal and Welch (2007) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this article,…
Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy
- Economics
- 2009
Welch and Goyal (2008) find that numerous economic variables with in-sample predictive ability for the equity premium fail to deliver consistent out-of-sample forecasting gains relative to the…
Neural network calibrated stochastic processes: forecasting financial assets
- Computer ScienceCentral Eur. J. Oper. Res.
- 2013
A novel method of “intelligent” calibration, using learning (2-layer) neural networks in order to dynamically adapt the parameters of a stochastic model to the most recent time series of fixed length (memory depth) to the past is proposed.
Forecasting the Equity Risk Premium: The Role of Technical Indicators
- EconomicsManag. Sci.
- 2014
It is shown that combining information from both technical indicators and macroeconomic variables significantly improves equity risk premium forecasts versus using either type of information alone, and the substantial countercyclical fluctuations in the equity riskPremium appear well captured.
Company Fundamentals and Equity Returns in India
- Economics, Business
- 2008
This paper examines the relationship between four company fundamental variables (viz. market capitalization, book equity to market equity ratio, price earnings ratio and debt equity ratio) and equity…
Equity Premium Prediction: The Role of Economic and Statistical Constraints
- Economics
- 2016
In this paper, we show that the equity premium is predictable out-of-sample when we use a predictive regression that conditions on a large set of economic fundamentals, subject to: (1) economic…
Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?
- Economics
- 2014
This paper shows that economic fundamentals can generate reliable out-of-sample forecasts for exchange rates when prediction is based on a "kitchen-sink" regression that incorporates multiple…