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chosen for the regression analysis, the overall validity of the Treynor ratio is dependent on the validity of the selected index as it relates to the underlying portfolio or fund. This is in direct opposition to the Sharpe ratio, which is self-contained, as it is based only on the historical returns of the portfolio or fund under review. Using the data previously calculated, we can calculate the Treynor ratio for the CAM small-cap value portfolio and the median small-cap value fund: TR(CAM SCV) = APR RFR 9.73 2.00 7.73 = = = 7.75 Beta* 0.9977 0.9977 APR RFR 5.01 2.00 3.01 = = = 3.04 Beta* 0.9912 0.9912

for ideas that Taleb has long espoused and talked about, and con rmed him as the leading doubter of theoretical conventionalisms Taken together, Taleb s take-no-prisoners critique and Simons clari cations, delivered within a few weeks of each other, appear as earthshatteringly impacting Surely, many had previously voiced complaints about the unworldliness of many a nancial theorem or a mathematical nance construct But, unless I recall incorrectly, such concerns proved only mildly in uential Yes, there was a modicum of public backlash against nance academicism after the 1998 fall of mega hedge-fund LTCM (which was co-piloted, after all, by two Nobel-endowed, Talebirritating star professors), but no blood was shed I don t think it would be accurate to portray those criticisms as in any way threatening to the academic status quo or to the continuous employment of quantitative professionals by Wall Street and the City of London.

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The number of cycles: For each combination of inputs and number of hidden layers, a series of models were built using 1,000, 5,000, 10,000, and 25,000 cycles. These values were selected in order to understand the curve as shown in Figure 7.35. As the number of cycles increases, the predictive accuracy of the model increases towards an optimal accuracy. Table 7.19 shows the r 2 values for different combinations of the three parameters. The objective when selecting the parameters is to keep the model as simple as possible with the fewest number of inputs and the smallest number of hidden layers at the same time as ensuring the model has not overtrained. The following model parameters were chosen to build the nal model. The model built using these parameters has one of the highest r 2 values: Inputs: Horsepower, Weight, Model Year, and Origin Cycles: 10,000 Hidden layers: 1 Learning rate: 0.2 The multilayer backpropagation neural network, as presented here, is one type of network. Other approaches are referenced in the further reading section of this chapter.

If anything, the weight of theory and the demand for quanty staff have only grown stronger in the past decade Similarly, nancial economics most illustrious device (the BlackScholes option pricing model) survived the 1987 crash pretty much unscathed, at least when it came to its perceived condition as a supremely popular and widely used tool Even though the Black-Scholes-inspired portfolio insurance strategies were a key driving force behind the largest single-day drop in equity markets, an unmistakable signal of the deeply problematic foundations of the formula, Black-Scholes was never blacklisted from business schools, textbooks, media coverage, or even trading rooms (quite the contrary, it was rewarded with the Nobel a decade later) For the past 20 years, we have basically continued trotting along, safe in the notion that Black-Scholes reigned supreme and was a wonderful discovery.

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Sure, its assumptions are way off base, but that has not been seen as a reason to throw it away, or doubt its position as the unchallenged standard tool Even the horrendous bursting of the Internet bubble in 2000, which emphatically highlighted the vastly unrealistic nature of one of nance theory s most orthodox tenets (that markets follow the Normal probability distribution and that rare events are extremely rarely present) as well as the impossibility of prediction, did not seem to make a dent into the perceived validity of quantitative nance Models that are founded on the Normality assumption kept forming part of the core curriculum at.

Volunteers were scanned while they watched these videos and tried to detect when the people in the video had a false about the weight of the box or when they were trying to deceive the viewer about the weight of the box. The posterior superior temporal sulcus was more active when the volunteers saw the unusually movements that occurred when the box was lighter than expected or when there was an attempt to deceive. This area may be concerned with subtle analysis of the movements that give us clues about others intentions.

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