Illiquid Instruments Research Articles

Dynamic Multi-Factor Bid-Offer Adjustment Model: A Feedback Mechanism for Dealers (Market Makers) to Deal (Grapple) with the Uncertainty Principle of the Social Sciences

The objective is to come up with a model that alters the Bid-Offer, currently quoted by market makers, that varies with the market and trading conditions. The dynamic nature of financial markets and trading, as the rest of social sciences, where changes can be observed and decisions can be taken by participants to influence the system, means that our model has to be adaptive and include a feedback loop that alters the bid offer adjustment based on the modifications we are seeing in the market and trading conditions, without a significant time delay. We will build a sample model that incorporates such a feedback mechanism and also makes it possible to check the efficacy of the changes to the quotes being made, by gauging the impact on the Profits.The market conditions here refer to factors that are beyond the direct control of the market maker and this information is usually available publicly to other participants. Trading conditions refer to factors that can be influenced by the market maker and are dependent on the trading book being managed and will be privy only to the market maker and will be mostly confidential to others. The factors we use to adjust the spread are the price volatility, which is publicly observable; and trade count and volume, which are generally only known to the market maker, in various instruments over different historical durations in time. The contributions of each of the factors to the bid-offer adjustment are computed separately and then consolidated to produce a very adaptive bid-offer quotation. The ensuing discussion considers the calculations for each factor separately and the consolidation in detail. Any model that automatically updates the quotes is more suited for instruments that have a high number of transactions within short intervals, making it hard for traders to manually monitor and adjust the spread; though this is by no means a stringent requirement. We can use similar models for illiquid instruments as well and use the quotations provided by the model as a baseline for further human refinement. We have chosen the currency markets to build the sample model since they are extremely liquid, Over the Counter (OTC), and hence trading in them is not as transparent as other financial instruments like equities. The nature of currency trading implies that we do not have any idea on the actual volumes traded and the number of trades. We simulate the number of trades and the average size of trades from a log normal distribution. The parameters of the log normal distributions are chosen such that the total volume in a certain interval matches the volume publicly mentioned by currency trading firms. This methodology can be easily extended to other financial instruments and possibly to any product with an ability to make electronic price quotations or even be used to periodically perform manual price updates on products that are traded non-electronically.Thankfully, we are not at a stage where Starbucks will sell coffee using such an algorithm, since it can possibly lead to certain times of the day when it can be cheaper to have a cup of coffee and as people become wary of this, there can be changes to their buying habits, with the outcome that the time for getting a bargain can be constantly changing; making the joys of sipping coffee, a serious decision making affair.

FAKTOR FUNDAMENTAL YANG MEMPENGARUHI PERUBAHAN HARGA SAHAM (Studi Empiris pada Perusahaan Finansial yang Terdaftar Di BEI Tahun 2009dan 2010)

This study aims to examine the fundamental factors of Current Ratio (CR), Leverage Ratio, Net Profit Margin (NPM), Total Assets Turn Over (TATO), Earnings Per Share(EPS)effect on stock price changes on financial companies in Indonesia . The share price is the value of a stock that reflects the wealth of the company issuing the shares, which change or fluctuation is largely determined by the forces of supply and demand occurred in the stock exchange (secondary market). The influence of these factors on stock prices is tested with regression analysis. Study sample is 40 companies listed on the Indonesian stock exchange are selected by purposive sampling.The results of this study indicate that the partial net profit margin of only variable that significantly influence the stock price, while the variable current ratio, leverage ratio, total assets turnover and earnings per shareno significant effect on stock prices. While simultaneously variable Current Ratio (CR), Leverage Ratio, Net Profit Margin (NPM), Total Assets Turn Over (TATO), Earnings Per Share(EPS)significantly influence the stock price. These results indicate that investors in making investment decisions take into consideration the level of stock prices, returns that would be obtained and also consider the ability of illiquid instruments (funds from third parties, received a loan of more than three months, and core capital) against liabilities (current debt ) company.
 
 Keywords: Stock Price, Current Ratio(CR), LeverageRatio, Net ProfitMargin(NPM), TotalAssetsTurn Over(TATO), Earnings Per Share(EPS)

Dynamic Multi-Factor Bid-Offer Adjustment Model

The objective is to come up with a model that alters the Bid-Offer, currently quoted by market makers, that varies with the market and trading conditions. The dynamic nature of financial markets and trading, as the rest of social sciences, where changes can be observed and decisions can be taken by participants to influence the system, means that our model has to be adaptive and include a feedback loop that alters the bid offer adjustment based on the modifications we are seeing in the market and trading conditions, without a significant time delay. We will build a sample model that incorporates such a feedback mechanism and also makes it possible to check the efficacy of the changes to the quotes being made, by gauging the impact on the Profits. The market conditions here refer to factors that are beyond the direct control of the market maker and this information is usually available publicly to other participants. Trading conditions refer to factors that can be influenced by the market maker and are dependent on the trading book being managed and will be privy only to the market maker and will be mostly confidential to others. The factors we use to adjust the spread are the price volatility, which is publicly observable; and trade count and volume, which are generally only known to the market maker, in various instruments over different historical durations in time. The contributions of each of the factors to the bid-offer adjustment are computed separately and then consolidated to produce a very adaptive bid-offer quotation. The ensuing discussion considers the calculations for each factor separately and the consolidation in detail. Any model that automatically updates the quotes is more suited for instruments that have a high number of transactions within short intervals, making it hard for traders to manually monitor and adjust the spread; though this is by no means a stringent requirement. We can use similar models for illiquid instruments as well and use the quotations provided by the model as a baseline for further human refinement. We have chosen currency markets to build the sample model since they are extremely liquid, Over the Counter (OTC), and hence trading in them is not as transparent as other financial instruments like equities. The nature of currency trading implies that we do not have any idea on the actual volumes traded and the number of trades. We simulate the number of trades and the average size of trades from a log normal distribution. The parameters of the log normal distributions are chosen such that the total volume in a certain interval matches the volume publicly mentioned by currency trading firms. This methodology can be easily extended to other financial instruments and possibly to any product with an ability to make electronic price quotations or even be used to periodically perform manual price updates on products that are traded non-electronically. Thankfully, we are not at a stage where Starbucks will sell coffee using such an algorithm, since it can possibly lead to certain times of the day when it can be cheaper to have a cup of coffee and as people become aware of this, there can be changes to their buying habits, with the outcome that the time for getting a bargain can be constantly changing; making the joys of sipping coffee, a serious decision making affair.

Dynamic Multi-Factor Bid-Offer Adjustment Model

The objective is to come up with a model that alters the Bid-Offer, currently quoted by market makers, that varies with the market and trading conditions. The dynamic nature of financial markets and trading, as the rest of social sciences, where changes can be observed and decisions can be taken by participants to influence the system, means that our model has to be adaptive and include a feedback loop that alters the bid offer adjustment based on the modifications we are seeing in the market and trading conditions, without a significant time delay. We will build a sample model that incorporates such a feedback mechanism and also makes it possible to check the efficacy of the changes to the quotes being made, by gauging the impact on the Profits.The market conditions here refer to factors that are beyond the direct control of the market maker and this information is usually available publicly to other participants. Trading conditions refer to factors that can be influenced by the market maker and are dependent on the trading book being managed and will be privy only to the market maker and will be mostly confidential to others. The factors we use to adjust the spread are the price volatility, which is publicly observable; and trade count and volume, which are generally only known to the market maker, in various instruments over different historical durations in time. The contributions of each of the factors to the bid-offer adjustment are computed separately and then consolidated to produce a very adaptive bid-offer quotation. The ensuing discussion considers the calculations for each factor separately and the consolidation in detail. Any model that automatically updates the quotes is more suited for instruments that have a high number of transactions within short intervals, making it hard for traders to manually monitor and adjust the spread; though this is by no means a stringent requirement. We can use similar models for illiquid instruments as well and use the quotations provided by the model as a baseline for further human refinement. We have chosen the currency markets to build the sample model since they are extremely liquid, Over the Counter (OTC), and hence trading in them is not as transparent as other financial instruments like equities. The nature of currency trading implies that we do not have any idea on the actual volumes traded and the number of trades. We simulate the number of trades and the average size of trades from a log normal distribution. The parameters of the log normal distributions are chosen such that the total volume in a certain interval matches the volume publicly mentioned by currency trading firms. This methodology can be easily extended to other financial instruments and possibly to any product with an ability to make electronic price quotations or even be used to periodically perform manual price updates on products that are traded non-electronically.Thankfully, we are not at a stage where Starbucks will sell coffee using such an algorithm, since it can possibly lead to certain times of the day when it can be cheaper to have a cup of coffee and as people become wary of this, there can be changes to their buying habits, with the outcome that the time for getting a bargain can be constantly changing; making the joys of sipping coffee, a serious decision making affair.

Conventional Investing in a Complex World

In the wake of market volatility, many investors may feel a desire to change their approach and be more “nimble” or “opportunistic” in their pursuit of investment goals. In this article, Robert Maynard, Chief Investment Officer for the Public Employee Retirement System of Idaho and Brandes Institute Advisory Board member, cautions investors who are thinking about abandoning traditional investment plans. He advocates policies that are simple, transparent, and focused rather than the increasingly popular “alternative” tactics, such as illiquid instruments and vehicles, leverage, and complex, opaque investment strategies.

Investment Considerations in Illiquid Assets

Alternative asset classes have varying degrees of tradability and structural liquidity. This article outlines illiquidity considerations as well as the pros and cons of investing in illiquid instruments and long dated trading strategies. Investing opportunity sets in inefficient market cycles tend to vary. Often, given market anomalies they come to reside for extended periods of time in less liquid instruments such as in distressed debt, private equity, certain types of loans, or in the securities of firms experiencing turnaround situations. These securities, because they are difficult to price, due to limited market participants, infrequent transactions, complex structures or highly uncertain future performance offer potential for excess returns over the risk free rate. Investors who have the ability to buy and hold these securities may thus stand to profit.

Back to the Future: Conventional Investing in a Complex World

In the wake of market volatility, many investors may feel a desire to change their approach and be more nimble or opportunistic in their pursuit of investment goals. In this paper, Robert Maynard, Chief Investment Officer for the Public Employee Retirement System of Idaho and Brandes Institute Advisory Board member, cautions investors who are thinking about abandoning traditional investment plans. He advocates policies that are simple, transparent, and focused rather than adopting increasingly popular alternative tactics such as illiquid instruments and vehicles, leverage, and complex, opaque investment strategies.

Liquidity Options

The financial crisis of 2008 revealed how critically important liquidity is to investors in times of turmoil. When investors and financial institutions were facing huge demands for cash that they either had to meet or else declare bankruptcy, security valuation that was derived from theoretical models became completely irrelevant. Tradable securities had to be dumped on the market and sold for whatever prices they could fetch. Looking back, one wonders: Can’t anyone figure out some way to hedge this risk using derivatives? Golts and Kritzman think they have. In this article, they propose the creation of a new kind of derivative contract they call a “liquidity option.” They describe how such options could be structured with payoffs resembling those of a cliquet option tied to some broad market index, and show how they should be priced to be consistent with the market’s current valuation of liquid versus illiquid instruments. <b>TOPICS:</b>Options, accounting and ratio analysis, VAR and use of alternative risk measures of trading risk

Hedge fund administrators and asset valuations – does it all add up?

PurposeThe purpose of this paper is to assess the role of hedge fund administrators, particularly in relation to valuing complex and/or illiquid financial instruments.Design/methodology/approachThe paper proceeds by way of an analysis of key trends and developments within the hedge fund administration industry in relation to the regulatory challenges posed by complex and/or illiquid instruments.FindingsThe paper argues that, because of inherent problems over the attribution of value to complex and/or illiquid assets, emphasis on independent valuation by administrators is largely misplaced. Recent events concerning valuation difficulties in the subprime mortgage market illustrate these very concerns.Originality/valueThe paper questions the ability of independent hedge fund administrators to provide reliable valuations for complex and/or illiquid instruments. Independent valuation of such assets suggests a level of scrutiny that is in fact not present. Moreover, the financial market meltdown surrounding the collapse of the subprime mortgage market provides a timely and salutary reminder that independent valuations of complex and/or illiquid instruments are inherently unreliable.