@DditsMas That is true. I also should add manias with a caveat. Also... theres time varying composition of distinct agents with varying time horizons ; risk attitudes/risk classifications ; styles etc. Bouts of predictability occur on during financial distress and manias because the mechanisms that creates "destructive nonstationarity" no longer exist and paths/dynamics become more certain due to time varying financial constraints on the critical agents to enforce "EM" results
@DditsMas No. A Quant Developer/Engineer. Maybe in a few years I'll try to improve on it and make it real time. Let's say I have a V0 product.. its years away
@DditsMas The best thing I've seen in the quant fund space without the need for (risky) predictive fin time serie modeling are mixes of portfolio diversification with risk scaling (volatility scaling).
@@DditsMas that's true for a lot of data. Any data that we choose to evaluate will only be a limited representation. That's why it's important to have a lot of diversity in your data collection - it gives you a more complete overview of things
Financial Time Series have really complicated structure. Only under extreme financial fragility, is there an increase in predictability.
Great point
@DditsMas That is true. I also should add manias with a caveat. Also... theres time varying composition of distinct agents with varying time horizons ; risk attitudes/risk classifications ; styles etc. Bouts of predictability occur on during financial distress and manias because the mechanisms that creates "destructive nonstationarity" no longer exist and paths/dynamics become more certain due to time varying financial constraints on the critical agents to enforce "EM" results
@DditsMas No. A Quant Developer/Engineer. Maybe in a few years I'll try to improve on it and make it real time. Let's say I have a V0 product.. its years away
@DditsMas The best thing I've seen in the quant fund space without the need for (risky) predictive fin time serie modeling are mixes of portfolio diversification with risk scaling (volatility scaling).
@@DditsMas that's true for a lot of data. Any data that we choose to evaluate will only be a limited representation. That's why it's important to have a lot of diversity in your data collection - it gives you a more complete overview of things