Effectiveness of earnings forecasts in efficient global portfolio construction

Robust approach to earnings forecast: A comparison

“…Earnings forecasts are used to evaluate firm performance and forming earnings expectations, and they have a central role in determining the intrinsic value of a stock (Brown, 1993; Gleason & Lee, 2003; Ohlson, 1995; Park & Stice, 2000). The fundamental variables of earnings forecasts are effective for constructing mean–variance optimal portfolios with significant active returns (Xia et al, 2015). Superior forecasts of earnings could provide an important advantage to investors in generating abnormal returns (Loh & Mian, 2006).…”

Comparison of Stock Selection Methods: An Empirical Research On The Borsa Istanbul

“…Methods for a stock selection to generate a successful portfolio vary from type to type, such as predicting future stock prices by studying their past patterns (Goumatianos et al, 2013), predicting risk by using size or book-to-price ratio as firm-specific characteristics (Lucas et al, 2002), comparing performances of previous stock selection methodologies based on liquidity, size, mean reversion, and momentum (van der Hart et al, 2003), applying learning-to-rank algorithms to understand investors' sentiment toward a group of stocks by comparing long-term and short-term performances (Song et al, 2017), using candlestick charts to predict future returns to generate a cherry-picked portfolio (Horton, 2009), using case-based reasoning (CBR) relying on fundamental and technical analyses to recognize winning stocks around earning announcements by comparing classification accuracy and Sharpe ratio (Ince, 2014), using false discovery rate (FDR) to examine the model selections to be used in stock selection (Cuthbertson & Nitzche, 2013), using abnormal news volume information and rate of analysts' attention toward targeted stocks to detect golden stocks for generating a global portfolio (Gillam et al, 2015), analyzing stock-buying or stockselling actions of mutual fund firms (Ratanabanchuen & Saengchote, 2020), using Markov decision process on genetic algorithms to define trading strategies (Chang & Lee, 2017), using fuzzy model based on fuzzy ranking (Tiryaki & Ahlatcioglu, 2005), using chaotic bagging indicator to select risk-averse actions to allocate stocks (Suzuki & Okhura, 2016), using consensus temporary earnings forecasts (CTEF) data (Xia et al, 2015), combining analysts' forecasts, momentum data, and fundamental ratios of firms into a model (Guerard et al, 2015), investigating large shareholders' behaviors toward listed stocks (Sun et al, 2020), and using Gordon model improved with multiple criteria decision making (MCDM) model (Lee et al, 2009). Thus, the purpose of all stock selection methods defined above-and similar methods based on statistical, linear regression, fuzzy analyses, cluster analyses, and weighted average stock selection (Yang et al, 2019), and the methods revealed in Section 2-is generating higher returns and providing positive-alpha situations at least in the portfolio management business.…”