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It should be remembered here that our sample period includes the dramatic stock market declines of the 2008 financial crisis. The estimated coefficients for the News Count and VIX in specification 4 are still statistically significant and have the same signs as in specification 3. The combined explanatory power of the right hand side variables in this super regression leads to a R2 of 0. 38. Gherzi et al. 2014 also find increased attention after both positive and negative large daily returns which they call the meerkat effect. This leads them to dismiss the ostrich effect. However, we first note that some of their results for weekly returns are consistent with the ostrich effect. Thus, their evidence on the ostrich effect is actually mixed. Second, and more importantly, our interpretation of the data differs from theirs and is based on a much larger sample. We argue that multiple factors are at play, one of which is the ostrich effect.

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Please find the link for github from belowAlong with the above, if readers would like to see some ideas from the images instead of content then please visit my Pinterest from below link where some creative ideas are being sharedPytorch Model is saved either in . pt or . pth format. To save the model, I defined checkpoint directory where the model is saved. torch. save will save the model. In the code provided in the GitHub link simple way to save and load model in pytorch, I have used a dictionary where both model state and optimizer is saved. Please have a look at below screenshot. Since, one has saved model in the previous step, one can load the model using torch. loadcheckpoint file. Process is very simple.

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