演算法投資
本帖最後由 sec2100 於 2019-3-2 19:25 編輯以下節錄包自於ZEROHEDGE的一篇文章，連結如下:
https://www.zerohedge.com/news/2 ... -algorithmic-trader
The way that mortgage-backed securities precipitated the financial crisis is very much applicable here. One of the fallacies behind that phenomenon was the assumption that the world would behave in the future the way it had in the past. For instance, housing prices would go ever upwards.
That fallacy is intensified in the case of quantitative investing, because all quantitative models use historical data to train themselves. As these techniques become more widespread, the assumption that the world will behave in the future the way it has in the past is being hard-wired into the entire financial system.
同樣一篇文章，但版面較好:
https://logicmag.io/06-money-machines/
Is Uber worth $60 billion? Well, Uber is worth $60 billion because we believe someone is willing to pay $60 billion for it. But maybe Uber is worth zero. Maybe that’s the actual value of the revenues that Uber will make in the future. In the current environment, we rely on liquidity to sustain prices for financial assets. When liquidity dries out and you’re forced to rely on the things that those financial assets actually represent, however, you could see painful shocks if there’s a big disconnect between price and reality—the kind of shocks you saw during the financial crisis. If the underlying computer models are less sensitive to measures of fundamental worth, they can create very large distortions in the prices of financial assets. You don’t need computers to do that, of course. You can have the Fed making a lot of cash available to everyone, cash that needs to go somewhere, and assets appreciate in response. Computers can do something similar. They can assume that prices will behave the way that their models tell them they’ll behave, and therefore drive prices to a point that is extremely disconnected from the things those prices are supposed to represent. On February 5, 2018, the stock market fell off a cliff. The Dow industrials dropped nearly 1600 points, its worst intraday point drop in history. In the aftermath, there was a lot of discussion about the role of computerized trading in triggering the crash. Is this a preview of the world to come? Should we expect more of that in the future?
There are certainly forms of instability that have been introduced by algorithmic trading that will increase as we put more and more faith in these algorithms. The February 2018 flash crash was instructive. The culprit was a slightly esoteric exchange-traded product that has a rebalancing mechanism inside of it. And that rebalancing mechanism ended up destroying the product on one specific day when the market moved a little bit more than the product was designed to handle. The product was required to trade a lot of instruments in response to that move. But then those trades exaggerated a small move and it became a big move, which required more rebalancing—and everything spiraled out of control. What about the impact of a more algorithmic financial system on retail investors? We’ve mostly been talking about big institutional investors, which makes sense because that’s where the money is. But how do these types of tools filter down to the ordinary investor who’s maybe got a small retirement fund?
You’ve already got robo-advisors, which use algorithms to manage assets for retail investors. We’re also probably only a few years away from you being able to log into a brokerage account and run a sophisticated institutional-grade algorithm yourself.
People tend to assume that the diffusion of these technologies is a good thing. I’m more ambivalent. I think it could be a big mistake to have the population at large play around with algorithms. Some people who are very good at it might benefit from having access to this broadened toolset. But most people will just end up paying too much or make bad decisions because they’re being given access to a technology that they aren’t equipped to do anything useful with. They can lose money with it, however.
科技公司更會處理大數據，更適合插旗金融業，中國的阿里巴巴已經在做
We’ve talked about the extent to which large financial firms are becoming tech firms. But I expect that it’ll start accelerating in the other direction: big tech firms will become financial firms.
If you’re a tech firm, why would you assume that a traditional financial firm is better at tech than a tech firm? If we’re talking about using big data and machine learning, well, tech firms have been doing that for a while. They’re better at data structure and organization and processing than anybody. They’re also newer, so they probably started off with better architecture internally.
In China, this is already happening. Large Chinese tech firms like Alibaba are much further along in their integration into the financial industry than their equivalents in the US. They’re doing payments and deposits and loans. The regulatory structure is more permissive. Given the expected growth of financial services there, it’s likely also a more attractive investment than for large tech companies in the US. Entrenched incumbents may be harder to dislodge here.
What financial firms have is a large customer base, which can be sticky. They also have a lot of unique knowledge—customer, economic, regulatory—from their position in the economy. But Google and Facebook have a ton of information they can employ for the same purposes—I mean, it’s hard to compete with the sheer quantity of data that tech firms have, or the scale of their integration into people’s lives. Retail investors have to put their money somewhere. They’re currently putting it into traditional financial firms. But there’s no reason that Google and Facebook shouldn’t be accepting deposits, facilitating payments, making loans, managing assets, running quantitative investment funds.
Everything I’ve described to you in the field of quantitative investing, I would imagine those companies could do very quickly. The data, the analysis, the algorithms, the infrastructure. The only question is why they haven’t yet. Anyway, over time I migrated to the investment strategy part of the financial world. I started helping large asset owners—entities like pension funds and sovereign wealth funds—allocate their money to systematic investment programs. That’s where I migrated to because that’s where most of the financial world was migrating to after the 2008 financial crisis, as everyone realized that the old ways of investing were not really doing what they wanted them to do.
Portfolios had been too exposed to the same underlying risks. Technology was now enabling investors to understand their risks better, and to take more direct control over their investments. Part of the shift involved removing human decision-making when it wasn’t perceived as adding any value.
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