Can value-based stock selection criteria yield superior risk-adjusted returns: an application of neural networks

Expert System Portfolios of Australian and UK Securitised Property Investments

“…Given the lower volatility of large stocks relative to all stocks, value portfolios comprised of large stocks are shown by O'Shaughnessy to typically outperform the market index by a sizeable margin on a risk-adjusted basis. Using neural network techniques to predict value stocks using the ratios identified by O'Shaughnessy, Eakins and Stansell (2003) demonstrate the superior performance of the neural network value portfolio versus the S&P 500 and Dow Jones Industrial Average. Following the work of Eakins and Stansell (2003), Ellis and Wilson (2005) recently investigated the performance of value portfolios comprised of Australian real estate stocks using a similar neural network methodology to identify individual value stocks 3 .…”

“…The current paper uses as its expert knowledge inference set the outcomes from the research of Haugen (1995), O'Shaughnessy (1998), and Eakins and Stansell (2003) to select a group of fundamental financial ratios that will be used to determine sets (portfolios) of 'value' securitised property assets. These fundamental variables form the inputs to a rule-based expert system that has preset constraints to isolate 'value' assets.…”

“…Rather than the CAPM, which has not been shown to accurately reflect investor sentiment towards risk (Eakins and Stansell, 2003), risk adjusted performance relative to the Australian market index is measured first by the Sharpe ratio (Sharpe, 1966), and second the Sortino ratio (Sortino and Forsey, 1996;Sortino et al, 1997) for adjusting returns on a downside risk basis.…”

“…Despite the fact that the AUD/GBP exchange rate is readily observable on a 24-hour basis, information pertaining to other factors (eg. PTBV, PC) may not be readily known (Eakins and Stansell, 2003). Stock specific information pertaining to the first-order autocorrelation structure and mean excess percentage returns for each factor for the year prior is also included.…”

Forecasting Purpose Data Analysis and Methodology Comparison of Neural Model Perspective

“…However, as a part of traditional supervised learning, we used an artificial neural network (i.e., ANN) model that uses labels as data. There are many examples [5][6][7], based on forecasting purpose data research, which are sometimes correct. However, this approach is worth investigating even the results are not correct.…”