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Evan and I talked about ask/bid spread, which appears to be the same as slippage, yes? I had no idea what this was until a few hours ago, and appears to be something that would be v imporant to incorporate. Assume that a stock has a listed closing price of X is there a reasonable function to get an estimate of the price I would be expected to sell at, assuming I sell at close? Im learning that it is slightly smaller than X, but Im wondering if its possible to estimate it quantitatively.

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slippage and bid/ask are totally different. bid/ask is the spread between where you can buy and sell a block of shares at any given point in time. slippage is how much the market moves against you while you are executing. for very small traders, fixed commissions tend to be their biggest concern. once your account gets bigger, bid/ask becomes the dominant 'cost' to your trading. for institutional-sized accounts, slippage winds up having the largest effect.

basically, until you wouldn't be comfortable sending your trade to the exchange as a single order because it probably won't get filled, slippage isn't really something you need to worry much about. at least not until something bad happens and market liquidity dries up.

i'm sure someone who knows more than i do about single-stock trading can quote you an average bid/ask for large caps.

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Given the debate over the EMH, its v odd that the model could predict the top 100 performers for the next day so well; it would stand to reason that if the model were accurate, that the EMH ought to be discared, yet it hasnt been. But, again, I just couldnt find the error. If anyone knows matlab well, Id be haappy to comment/clean up the code and have them audit it.

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EMH only needs to be discarded is your system is actually implementable in a way that would make money. trading at the same price (the close) you use to make a decision is a good way to make your system unrealistic. start by modifying your program to include a one-day lag for trading and a linear transaction cost for bid/ask; if your system is still profitable after those changes, then this becomes a much more interesting discussion.

edit: also, to nitpick, when you do your out of sample testing, you should train your coefficients on the older data set and look at the results on the newer set.