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Understanding how certain players or groups of players are positioned might also generate some useful information (for example, certain pension funds that target a strategic policy allocation might have to buy equities …

Beta as a measure of risk has serious drawbacks, particularly in emerging markets. You need to consider alternative risk metrics (cost-of-capital build-up method or volatility, for example), or if you …

You should consider an unsupervised learning algorithm such as K-nearest neighbor ('KNN'). KNN will measure the distance amongst the observations in your space. You can and probably should consider a …

Let's start with an empirical test of the relationship between leverage and stock price returns and see if this connects to corporate finance theory. Below is a bivariate chart that show the one-yea …

Great question. We would expect 3rd party risk providers to have specialized expertise (robust regression techniques, factor research, data cleansing etc.). We might grant them these advantages but st …

Yahoo data is not as clean as alternative data providers such as CSI. Some issues are: i) data for some tickers is missing, ii) incorrect data (rare but it does happen), iii) sometimes they fail to me …

Stop loss simulation with strictly OHLC is not possible since you need to assume the ordering of the high and low (which one came first)? You can make an assumption when there is a straight line thru …

Take a look at Campbell's 2008 paper "Predicting Excess Stock Returns out of Sample". This paper is in response to Goyal & Welch's paper which argued that excess returns cannot be predicted out of sam …

Measuring expected shortfall (also known as conditional value-at-risk) answers the simpler question of "what is my average expected loss at the i-th quantile?" given the empirical distribution of retu …

You can forecast stock prices thru time-series models, cross-sectional, or panel models. There is considerable variation within these categories. In time-series models you would use an auto-regressiv …

I would normalize valuation variables over the business cycle. These normalized variables exhibit mean-reversion. For example, use price-to-peak earnings rather than P/E. Here is a good illustration o …

Check out A Blocking and Regularization Approach to High Dimensional Realized Covariance Estimation. Abstract: We introduce a blocking and regularization approach to estimate high-dimensional co …