Just finished reading the paper Stock Market’s Price Movement Prediction With LSTM Neural Networks. The abstract attractively reads: “The results that were obtained are promising, getting up to an average of 55.9% of accuracy when predicting if the price of a particular stock is going to go up or not in the near future.”, I took the bait. You shouldn’t.
The evaluation of volatility models is gracefully complicated by the fact that, unlike other time series, even the realization is not observable. Two researchers would never disagree about what was yesterday’s stock price, but they can easily disagree about what was yesterday’s stock volatility. Because we don’t observe volatility directly, each of us uses own proxy of choice. There are many ways to skin this cat (more on volatility proxy here).
In a previous post Univariate volatility forecast evaluation we considered common ways in which we can evaluate how good is our volatility model, dealing with one time-series at a time. But how do we evaluate, or compare two models in a multivariate settings, with two covariance matrices?
Perhaps it is the different jargon used in different disciplines, not sure. But for some reason, the terms ‘predictions’, ‘forecasts’ and ‘projections’ are frequently used interchangeably.
One of my Ph.D papers was published recently. It deals with yield curve forecasting.
Here is the code for applying the Nelson-Siegel model to any yield curve.
“The Fed is certainly moving forward with plans to normalize interest rates.” We keep on hearing that, we believed it in the past and we believe it now. We believe that the Fed believes and that, in fact, this means something.
Should we become more suspicious and less trusting given history? Let’s take a look.
Overfitting is strongly related to variable selection. It is a common problem and a tough one, best explained by way of example.