Tag Archives: trading

The Hidden Cost of Credit Ratings

justice

The NY Times’ “The Hidden Cost of Trading Stocks” paints a concise and damning picture of yet another malpractice in financial services.  This has been a recurring theme.   

Another storm may be brewing – this time for the credit ratings industry.  

It is standard practice for issuers to hire ratings agencies to rate their securities.  This practice has lead to incentives to give higher ratings when trying to get business from securities issuers, putting the ratings agencies in the position of representing the issuers when they are given special status to serve and protect investors.  They are not even required to disclose the conflict of interest.  The closest we get to protection is a lawsuit when they explicitly advertise objectivity.

It now looks like 16 states will each get their chance to sue individually.  This may be the beginning of a big and positive change.  If conflicts of interest did influence credit ratings, it would shift capital and damage economic efficiency even when it is not misdirecting pension money into a mortgage bubble.  I wonder if we will see a social media movement to influence reform like we are seeing with network neutrality and “common carrier” status.  I hope so.

 

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LinkedIn IPO: the Public Premium

Facebook has been leading a cult of companies avoiding IPOs.  The claim is that the cost of regulation and transparency is unnecessary and inefficient.  Private markets like Second Market have grown tremendously.  This may be all wrong, and let’s hope so.

The IPO of LinkedIn demonstrated a public premium: public markets offered a higher valuation than the private markets did.   Valuations can be higher because discount rates are lower.  Think about it: public investors get maybe 15% in a strong year.  Private Equity investors are organized around discount rates of 20% or higher.    If your discount rate is so high, future profits are simply not worth as much.

There is another important reason for a public premium: regulated standards of conduct and transparency.  When owners (shareholders) are more informed and confident, risk is reduced and value goes up.

The reason to hope this is the case is for the public good.  If IPOs and public listings are shown to be a rational — because the cost of compliance is less than the valuation premium — then more companies will be public and capitalism will be more broadly accessible. Also, this will lead to higher overall investment rates and stronger economic growth.

AI and Program Trading

NewScientist published a good article describing neural network program trading systems.

An extension allows for a large number of competing signalling systems. One such signalling system may be an improvement on traditional cointegration techniques.

Cointegration improvements

Cointegration typically uses the price information for two related securities, and provides relative value signals. As with traditional technical trading strategies, changes to the fundamentals create a risk of bad relative value signals. With 2-security cointegration, this risk is doubled because changes to the fundamentals of either company can skew the relative value signal. However, this problem can be cut in half by creating baskets (portfolios) of securities and running the cointegration analysis with each security against the basket. This effectively generates signals which are skewed only by changes to the fundamentals of the individual security. Additionally, the required correlation matrix for the permuted set of security combinations can be replaced by a single vector of correlations – greatly improving calculation efficiency and extending the analysis processing potential.

To improve upon normalizing data to % changes, factors typically associated with beta may also provide better signalling data. For example, as the size of a company grows over the course of a few years, its price volitility may fall. Similarly, as market cap grows, the price change correlations may increase relative to larger cap baskets and decrease relative to smaller cap baskets.

By backtesting, optimal trigger strengths and bet sizes can be measured, however, given the correlation coefficients, volitilities, number of positions, and risk preferences, probabalistically optimal bet sizes may provide better results.

Financial Markets Evolve

Arbitrages – even of very small marginal size – will be eliminated based on a large number of artificially intelligent program trading systems that will mine the historical and currently released information identifying and exploiting trends. The process of the elimination of arbitrage opportunities will create vast concentrations of wealth within the companies that embrace the tools that automate this process. As new information sources become available for analysis, new arbitrages may be identified with increasing complexity. The abstraction of trading systems to automaically test and integrate new data sources will mark the last decades of financially advantageous investment in hedge funds. After that time, return will be a stochastic function of expected risk.

Artificially Intelligent Program Trading System


Data Sources

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Parallelized Analytics Agents

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Signal Aggregation analytics

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Trading Platforms and Reporting

Arbitrages – even of very small marginal size – could be exploited based on a large number of artificially intelligent program trading systems mining historical and currently released information for financial products and correlated factors. Using multiple signalling agents, aggregated signals can be calculated by weighting each agent based on the statistical strength of historical signal combinations. Using this type of design, it is easy to develop multiple agents that are completely independent and confidential. Multiple agents can be developed concurrently, tested, and introduced to the aggregate system at any rate.

The process of the elimination of arbitrage opportunities will create vast concentrations of wealth within the companies that embrace the tools that automate this process. As new information sources become available for analysis, new arbitrages may be identified with increasing complexity. The abstraction of trading systems to automaically test and integrate new data sources will mark the last decades of financially advantageous investment in hedge funds. After that time, return will be a stochastic function of expected risk.