Assessing Predictability in Trading Desk P&L

# Predict the behavior of a time series (P&L trading desk)


# Predict the behavior of a time series (P&L trading desk)












I work at the trading desk P&L department at a large bank. The trading desk has positions in almost all sorts of derivatives (options, futures) over a long list of stocks, currencies, commodities... My boss asked me to do the following:

1) Show that it is not possible to predict next years P&L

2) Build a working econometric model to predict next year's P&L

The daily P&L time series is really not stationary.

Can I tell my boss that:

Non-stationarity $\Rightarrow $Cannot be predicted based exclusively on the time series. Is it true? At least to start and show him that the time series is not enough to make predictions and that we will need, at least, a complex machine learning model to do so.

## Answer by Chris Taylor (score 7)

https://quant.stackexchange.com/a/35203

Without seeing your trading desk's P&L it's impossible to say whether it is predictable or not. But here are a few thoughts -

- There's no reason to think that it isn't predictable. In general, financial time series are hardest to predict when the represent the return stream of an investible asset. A trading desk's P&L isn't really investible, so there is no reason to think that it will be unpredictable.

- You certainly won't be able to predict the P&L with a large amount of certainty, but you may be able to make ballpark estimates.

- Have you graphed the P&L to check if there is an obvious trend? For example, if markets are getting more efficient, you would expect P&L to be decreasing over time.

- Is P&L correlated with other variables that you may be able to predict? For example, if the desk is engaged in market making, it will probably make more money when volatility is high and liquidity is low (as the desk is selling liquidity, and the price of liquidity is high when markets are more volatile).

- If the desk is trading illiquid instruments (e.g. exotic derivatives) the P&L series may be autocorrelated. This is especially true if P&L is correlated with volatility or liquidity, since vol and liquidity measures are autocorrelated.

- Are the markets the desk is engaged in becoming more heavily traded, or less? P&L from market making is correlated with volume, and you may be able to make a reasonable guess about whether volume will be higher or lower next year.

What you certainly do not need is a complex machine learning model. As Lisa Ann said in a comment, you will do much better with visualisations, simple linear models, and a healthy dose of insight.