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Meta-economics: Stochastic & Nanotech for Nanotech Commercialization and Corp Fin Analysis of Nanotech Companies

http://learnpythondatasciencenyc.site/

http://bigdatascienceblockchainnyc.site/

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http://joshiprogrammingclassnyc.site/  

http://qcfinanceconsultingnyc.site/

http://excelvbadataanalyticsnyc.site/

Book on Nanotech Commercialization that includes Satyadhar and Arpit as contributers - two of the members at Qcfinance.in

This books talks about the strategy to commercialize nanotech and use of stochastic mathematics to visualize application in Nanotech product realization. The second part of the book talks about Corporate Finance Models for 3M (A nanotech company) and uses Quant capital structure models to project share holder returns in various strategies and analysis different volatilities that effect the valuation. 

The book would be available from Jan'15.

Nanotechnology (Tech) Investment Banking - Corporate Finance Perspective

Nanotechnology (tech) Investment Banking - Corporate Finance Perspective

Nanotech in Semi-Conductor - display HDD ram processors etc

Nanotech in Defense - Sensors - Infra - strong material

Nanotech in Energy - Solar Fuel cells

Nanotech in Bio-medical - artificial body parts - drug delivery

Finding financial data implications.

ETF Angle - we would do selection - rebalancing on Quant strategy for the nanotech companies - Investors in the nanotechnology companies

Capital Structure - Advise company

Links:
Peers from Yahoo finance:
http://finance.yahoo.com/q/co?s=MMM+Competitors
https://in.finance.yahoo.com/q/co?s=AMAT

Nanotech ETF companies to get peers:
http://www.nanalyze.com/2014/06/what-happened-to-the-merrill-lynch-nanotech-index/
http://www.investorguide.com/etf.php?symbol=PXN
http://www.etfcommentary.com/nanotech-etfs/

http://innovate.gatech.edu/success-stories/green-nanotechnology-investment-researchers-assess-economic-impact-nanotech-green-sustainable-growth/


Semiconductor Companies in India:
http://www.efymagonline.com/pdf/EFY-Top100_Oct09.pdf
http://www.bseindia.com/markets/Equity/EQReports/IndustryView.aspx?expandable=2&page=20201001%20&scripname=Consumer%20Electronics
http://pradeepchakraborty.blogspot.in/2007/08/top-10-indian-semicon-companies.html

Defense Investment Banking - Abstract of Papers on India-Israel Relations

India-Israel Defense Relationship: Quantitative & Qualitative Analysis of Defense Companies of India & Israel

Abstract— when it comes to India-Israel relations, the most talked about aspect has been the defense relations between the two nations. Here we have tried to study all the aspects of defense relationship and compared defense industries of India, Israel & US. After the establishment of formal relationship between India & Israel, the defense relationship has grown manifold. Israel is now the second largest source of weaponry for India. Thus, analyzing this relationship was important. We have come out with the model which can predict how the future relationship can be for defense trade between the two nations. Defense trade and investment between the two nations is bound to rise exponentially. 

  We have used three modeling aspects - Multiple Regression on EV/EBTIDA multiple, Projections of share price by Monte Carlo Simulation and Rank Correlation (current year). We find that contribution in multiple (based on MLR) is more from debt and intercept in the companies of Israel and USA. Indian companies are not very good in using debt as tool to amplify shareholder returns. We found that optimizations and controlling sales volatility for Israeli companies and right use of debt for Indian companies can increase the returns. Also we found that margin and multiple have remained stable and is not causing change to the share price as sales volatility.

Keywords— India, Israel, US, defense, relations, sector. 

Presented the Paper on India-Israel Defense Relations at Rajiv Gandhi National University of Law, Patiala.

Conference Name: International Conference on Indo-US Relations & South Asia.

Conference Date: 29th Oct. 2014 (Wednesday).

Conference Link: http://www.indous.asia/

Quant Techniques & Regimes for Increase in Trade & Business Between India & Israel

Abstract

In this paper we have performed Monte Carlo Simulation, Multiple Linear Regression and Regime analysis for Projecting Trade & Business between India and Israel. The trade relations between India and Israel are extensively analyzed and predictions are being made using various mathematical models for their expansion. The effects of political regimes on economic relations are also evaluated. We have used regression and AR 1 models to predict imports (indexed over a broad range of products). The factors like High Tech machines & chemicals, which constitute most of the trade, are assumed as market parameters and are being utilized in forming multiple regression model. Two parameters that remain important in predicting import are exchange rate (currency) and market (Sensex) trends. In this work we also ran multiple regressions on Imports based on various factors and developed a multiple regression model. We have projected Import for the next 50 months and projected bilateral trade based on the import and changing the trade/import parameter. The deficiencies of trade between India and Israel are analyzed and also scenarios and segment drivers for the trade are also explored. We also studied the impact of terrorism and war on the trade and relations between India and Israel. In this regard, market movement in major terrorist attacks is compared and a stress testing methodology which could help us predict how markets would behave in case of another terror attack is proposed. We also pointed out areas where the cooperation could grow and use of Private Equity, joint ventures, and the financial aspects of Return on Equity for the same. 

Excess Cash Projections

Excess cash projections required a lot of assumptions as compared to share price where we have used only three parameters and used different stochastic models to compare the results.

Assumptions taken are “point in time” and are made of: CFO, Debt, CFI, Dividend, Cash, and Number of shares. In the list only CFO remains fixed and all other changes are due to policy change in our model.

As expected the model is not as stable and the projections of excess cash is dependent on too many assumptions where CFO is the toughest to project. Distribution and return of capital can be modeled using the excess cash projections where we can also tweak the amount of Repo, dividend and debt. The problem of the upward biases in the consensus estimates remains the challenge to deal with as no stochastic process can be applied to any of the processes used in cash projections.

While we might see that different companies may value the excess cash in a different way - to get an idea of this - we should look at Return equation regressed with factors that include sets of leverage, return, market, risk, efficiency and based on our findings we can project what to do with the excess cash - so there are two ways to look - company specific and sector specific way.

And there are research that try to predict the excess value of cash of $1 which may vary from .25 to approx. 2.

Ways to look at cash to get at optimal value:

·         

•            Sector story - each sector has different dynamics.
•       Growth story - payout story - return story - numbers should be same to get a good idea.
•       Leverage and Market cap story.

Beyond the three – which matters most in projections – How would Acquisition hit the multiple

Out of the three volatilities, which would affect a company most? This answer can help a company control that part, example if its Share price, they can do Repo, if its margin, then they can research the past mistakes & can try to correct them, if it is Sales then a company can look into it. Although the general idea is to focus on sales and margin is a particular way to keep sales high and margin low, this might back fire and increase the volatility, so long term stability should be in the focus.

Beyond the three – which matters most in projections?

1.      CFO projection

2.      Dividend Projection

3.      Remaining cash that accrues?

4.      Dep – Capex – PPE can be ignored.

Projection of EV/EBITDA and EBITDA post acquisition, will market add the growth factor in your multiple?

Repo vs. Acquisition:

• The Minimum elements needed for projection of the company we acquire?
• Post-acquisition will market change our multiple?
• Doing Repo will cut our future growth prospectus?
• Which sensitivities must we take?

Would a multiple regression help on multiple help? If the multiple is correlated with growth of sales and capex the next acquisition will reward us with better multiple, also if it is negatively correlated with growth, it will reward us (spending cash is a good idea) but if things are reverse the acquisition will not help us.

Discretionary cash at use can be used for many things which are like dividends (which should be paid), capex to keep PPE intact and other things. If we can get the NI Margin we can then get the NI and then the cash after reducing FCI and FCF and then use the cash as excess cash. The extra cash is a trouble for company in many ways and they look for options to remove that cash, but from a modeling sense reaching their required lot too many assumptions.

Stochastic Paths for EBTIDA – Pricing Debt – Rating – D/EBTIDA Multiple

• Consensus + past volatility AR1.
• Pure AR1.
• Jump + AR1.
• Mean Reversion.
• Multiple regression and then taking AR1 for each factor that the EBTIDA depends on, for example if there are four factors like Margin, Sales, Capex… find the AR1 model / applicable model and then draw EBITDA from there.
• Logistic regression on growth (Yes/ No) if yes how much.

Along with these, many more.......

Based on the above factors for the company or the sectors we can predict the EBITDA and take current Debt to find out where the D/EBITDA multiple would move. Seemingly wrong model can give us an overvalued projection.

Debt that a company takes is callable convertible and hence it has an option to convert and call… In that case the cost of debt would differ and the IRR for the company would change.

Debt options in convertible are many, but it is interesting to note that share price may trigger convert whereas dividend or other ways to return capital don’t. Getting cash would require making BS and NI, both of which would move into accounting. Once the ratio is checked we need to check the dilution using Black Scholes Model, where a company would hedge its risk on a convertible bond (not keeping it callable or keeping it callable for duration). If the conversion is optional at the end and the price is reached, than conversion causing dilution should be checked with hedges that are taken. In this regard both MC on stock price, E/EBTIDA, and BS for pricing of call option (that a company would purchase needs to be calculated). BS model would land us with price of the hedge moreover since our analysis is focused on share price and we keep the volatility same we can use the Merton model to find out the bond pricing as well as the PoD would change as stock price will move away, again the volatility of the stock would be calculated on a daily basis whereas the price will be calculated using our MC simulation. This would give us the implied PoD which can be used to model cost of debt. The other way to check if the multiple falls beyond a range where it would be tough for us to maintain the rating.

Quant Corporate Finance – Research Methodology - Monte Carlo Simulation, Share Repurchase & Multiple Assumption

Stochastic process AR-1 is used for sales figures on past quarterly data. Another methodology suggests that we should match our projections with the consensus but relying on either (stochastic model or the consensus) can create errors in the projections. As we know that past volatility play a very important role in the path generation we should always use sales after removing the seasonality. Hence a judicious role here is important.

Dual effects of share repurchase offers features that are not offered in conventional ways to return to shareholders are:

• Volatility / Volumes of share are affected. High volumes bring in our shares in some good index and thus increase trade and liquidity of our stock. We can control volatility of the shares by controlling the down side of the shares using ‘buy on low share price’.
• Best way to give back to shareholders as you kill the number of shares after repo, may be dividend or retained money will not show that much effect over share prices which market don’t react.

Share Repurchase Mechanisms: This could work when we buy shares based on some logic like P/E fall or just the price falls. Price falls and ratio are two different styles where one method is based on input from the market and other is a passive strategy.

P/E and price falls makes the most sense because we can buy the share when market or other quant indexes are short whereas a company we believe on our own fundamentals. In most of the research we have observed that P/E shows the best result because we buy undervalued and strategically ignore overvalued shares, thus we reduce the number of shares that shoots the value of our shares toward the upper side. Finally we would also increase the EPS by doing strategic buy back.

Modeling Considerations: We have used the last number and the grid for finding which repo amount we will flow through the model. In the grids we have used last number function to get the desired amount.

Let us take a simple example to understand the buildup of the model using three parameters:

Assume that we have: 100 shares, market price of $10/share which gives us a market cap of $1000. Furthermore let us assume that the debt is $100 debts, the current multiple is 11 and the EBTIDA is $10.

Hence EV/EBTIDA * multiple =1100

Now, if the company takes a debt of $200 its EV increase to 1300 (if it doesn’t keep it as cash). In our model we have kept the EV constant which means that the new multiple which should have been 1300/10 = 13 to keep the EV to 1300 comes back to 11. The assumption is that share price will react.

Why did the multiple moves and how will this happen in the real market?

Interestingly, in the case above the EV/EBITDA will not move, so that hit will be on Equity which will reduce by 200 which is the amount of new debt taken. In reality this reduction of MV will come as reduction of debt and MC the multiple will again hover close to the old value of 11 (which was the long term multiple). Thus either our debt value decreases or equity decreases or both decreases to keep the multiple close to long term average, thereby in real markets we can assume a combined effect of both on the multiple.

Results, Possible Improvements & Research Pathway

Effect of Share repo on company’s total return for the projected period depends on when we buy the shares. These results would include the effect of stochastic movements in three variables: Sales, Multiple and Margins keeping all other things status quo. Model takes us to a mean path which could be also used to project 90% confidence of remaining in the range X+-Delta, etc. Factors that is most relevant for deciding Optimum Capital Structure will include expected results in all strategies and whether we can increase leverage and still keep the Multiple intact. How does idea of algorithmic share buyback effect returns as compared to traditional dividend policies are shown to in this research.

Excel-VBA models were used create the scenarios and but this work could be extended to R/MATLAB. Modules: Acquisition, Pension Liability, and Share Repo’s effect on return and the best Capital Structure depends on projections of the core elements of company drivers and are dependent on share price which makes the engine useful.  Which assumptions matters the most and how to get other settings for getting realistic simulations can be judged but this research provides a directional indication. Sometimes the very minutatne of basis points contribution of selected policy might be done at the cost of greater leverage that should be avoided. In our models we have used only 10 paths that could be scaled ahead

Internal factors for a company for doing MC sim:

• ·         Sales
•        Multiple
•        Margin
•        EV-EBITDA

Market numbers:

• ·         Interest rate
•        S&P
•        Libor
•        Steepness

We would shake them up.