Monday, February 11, 2008
Books on Forecasting
1. Andrew Flight, Cash Flow Forecasting, Butterworth-Heinemann, Burlington, Ma, USA, 2006 (NITIE Lib access No. 658.15244)
Friday, February 8, 2008
Monetary Policy and Equity Returns in India
Rajiv Kalra, Narayana Rao, and Wolf examined the effect of monetary policy on equity returns in various countries. India is one of the countries.
Monetary Policy and Equity Returns: Evidence from Developing Countries
Rajiv Kalra, Narayana Rao KVSS and Jason Wolf,EUROPEAN JOURNAL OF ECONOMICS, FINANCE
AND ADMINISTRATIVE SCIENCES,April, 2007, pp. 166-181
The paper is available in and can be downloaded from
http://www.eurojournals.com/ejefas7.pdf
Monetary Policy and Equity Returns: Evidence from Developing Countries
Rajiv Kalra, Narayana Rao KVSS and Jason Wolf,EUROPEAN JOURNAL OF ECONOMICS, FINANCE
AND ADMINISTRATIVE SCIENCES,April, 2007, pp. 166-181
The paper is available in and can be downloaded from
http://www.eurojournals.com/ejefas7.pdf
Inflation and Equity Returns in India
Narayana Rao and Bhole examined the relation between inflation returns and equity returns in India. Based on the regression between monthly returns on RBI index and monthly inflation based on WPI they came to the conclusion that the relation was negative.
Subsequently Joshi examined the relationship between anticipated and unanticipated inflation and equity returns in India.
Subsequently Joshi examined the relationship between anticipated and unanticipated inflation and equity returns in India.
Wednesday, February 6, 2008
Consumer Confidence Index (CCI) - India
The ET-TNS Consumer Confidence Index (CCI) has increased by 5% to 128 points in the June ’07 ended quarter, compared to the March quarter.
The CCI is divided into two components — the present situation index (PSI) and future expectation index (FEI).
In the current round of the survey, the PSI has increased by 7%, while FEI has risen by 3% over the previous round. Both PSI and FEI measure consumer confidence according to three parameters, namely business conditions, jobs and income.
19 Jul, 2007
http://economictimes.indiatimes.com/Opinion/Todays_Features/ET-TNS_Consumer_Confidence_Index_Survey/Nothings_gonna_stop_us_now/articleshow/2216626.cms
----------------
The ET-TNS Consumer Confidence Index (CCI) - Method of Calculation
The ET-TNS Consumer Confidence Index (CCI), India’s first comprehensive measure of consumer confidence, was launched in December ’02.
The ET-TNS CCI is made up of two components: The Present Situation Index (how things stand currently compared to six months ago) and the Future Expectations Index (how things will be six months hence).
Each component is, in turn, based on three dimensions — general business conditions in the respondent’s line of work, availability of jobs and household income. For each dimension, we calculated the ‘relative value’, which is simply the difference between the percentages of positive and negative responses, plus the normalisation factor of hundred.
The CCI and its two components are the arithmetic averages of the relative values of the dimensions. A CCI at 100 implies a ‘neutral’ consumer. A value below 100, in theory, implies prevalence of pessimism and a value above 100 means there are more optimists than pessimists.
The CCI’s movement acts as a leading indicator of consumer behaviour and hence, of the overall business cycle. International experience shows that the CCI essentially indicates the direction of movement, rather than the actual quantum of consumer spending.
http://economictimes.indiatimes.com/Opinion/Todays_Features/ET-TNS_Consumer_Confidence_Index_Survey/How_we_did_it/articleshow/386977.cms
The CCI is divided into two components — the present situation index (PSI) and future expectation index (FEI).
In the current round of the survey, the PSI has increased by 7%, while FEI has risen by 3% over the previous round. Both PSI and FEI measure consumer confidence according to three parameters, namely business conditions, jobs and income.
19 Jul, 2007
http://economictimes.indiatimes.com/Opinion/Todays_Features/ET-TNS_Consumer_Confidence_Index_Survey/Nothings_gonna_stop_us_now/articleshow/2216626.cms
----------------
The ET-TNS Consumer Confidence Index (CCI) - Method of Calculation
The ET-TNS Consumer Confidence Index (CCI), India’s first comprehensive measure of consumer confidence, was launched in December ’02.
The ET-TNS CCI is made up of two components: The Present Situation Index (how things stand currently compared to six months ago) and the Future Expectations Index (how things will be six months hence).
Each component is, in turn, based on three dimensions — general business conditions in the respondent’s line of work, availability of jobs and household income. For each dimension, we calculated the ‘relative value’, which is simply the difference between the percentages of positive and negative responses, plus the normalisation factor of hundred.
The CCI and its two components are the arithmetic averages of the relative values of the dimensions. A CCI at 100 implies a ‘neutral’ consumer. A value below 100, in theory, implies prevalence of pessimism and a value above 100 means there are more optimists than pessimists.
The CCI’s movement acts as a leading indicator of consumer behaviour and hence, of the overall business cycle. International experience shows that the CCI essentially indicates the direction of movement, rather than the actual quantum of consumer spending.
http://economictimes.indiatimes.com/Opinion/Todays_Features/ET-TNS_Consumer_Confidence_Index_Survey/How_we_did_it/articleshow/386977.cms
Business Confidence Index Analysis in India
29 Jan, 2008
The RBI’s business expectation index for January-March 2008 declined 2.5 percentage points over the previous quarter from 50.2% to 47.7%. This is the lowest that the outlook on the business situation has touched in the past six quarters. Even the overall industrial outlook survey has dipped. These indices have dipped lower than the current levels only during 2002-03.
The indices are compiled after surveying over 1,000 corporates. The survey based on net response (difference between those who were optimistic and those who were pessimistic) on the ‘quarter ahead’ expectation about the industrial performance was less favourable for most parameters. This decline, according to RBI, was on account of lower net responses or lesser optimism for major parameters of the survey such as the overall business situation, availability of finance, production, order books,capacity utilisation, employment, exports and profit margins over the previous quarter.
http://economictimes.indiatimes.com/Indicators/Business_confidence_dips_on_global_recession_fears/articleshow/2739332.cms
Research paper on methodology for constructing index by NCAER-_india
http://www.oecd.org/dataoecd/11/51/33653943.pdf
The RBI’s business expectation index for January-March 2008 declined 2.5 percentage points over the previous quarter from 50.2% to 47.7%. This is the lowest that the outlook on the business situation has touched in the past six quarters. Even the overall industrial outlook survey has dipped. These indices have dipped lower than the current levels only during 2002-03.
The indices are compiled after surveying over 1,000 corporates. The survey based on net response (difference between those who were optimistic and those who were pessimistic) on the ‘quarter ahead’ expectation about the industrial performance was less favourable for most parameters. This decline, according to RBI, was on account of lower net responses or lesser optimism for major parameters of the survey such as the overall business situation, availability of finance, production, order books,capacity utilisation, employment, exports and profit margins over the previous quarter.
http://economictimes.indiatimes.com/Indicators/Business_confidence_dips_on_global_recession_fears/articleshow/2739332.cms
Research paper on methodology for constructing index by NCAER-_india
http://www.oecd.org/dataoecd/11/51/33653943.pdf
India - Economic Indicator Analysis
Articles and papers
http://www.oecd.org/dataoecd/54/48/34898082.pdf
Chitre, V. S.( 1982), " Growth Cycles in the Indian Economy ," Artha Vijnana, 24, 293- 450.
___ (1991), " Fluctuations in Agricultural Income , Public Sector Investment, World
Economic Activity and Business Cycles in India ", in H. Osada and D. Hiratsuka (eds.),
Business Cycles in asia , Institute of Developing Economies, Tokyo.
___(2001)," Indicators of Business Recessions and Revivals in India :1951-82", Indian
Economic Review , Vol. XXXVI, no. 1 ,2001.
Dua, P. and A. Banerji ( 1991 ) , '' An Index of Coincident Economic Indicators for the
Indian Economy", Journal of Quantitative Economics, Vol. 15, No. 2
____(2001) ,"An Indicator Approach to Business and Growth Rate Cycles : The case of
India ", Indian Economic Review, Vol. 36, No. 1 .
Hatekar, N. (1994), "Historical Behaviour of the Business Cycles in India: Some Stylized Facts for 1951-85", Journal of Indian School of Political Economy, Vol.6, No. 4.
Mall O.P. (1991),"Composite Index of Leading Indicators for Business Cycles in India ", RBI Occasional Papers , Vol 20, no. 3.
Nakamura J. (1991), "Fluctuations of Indian Economy, " in H. Osada and D. Hiratsuka
(eds.), Business Cycles in Asia , Institute of Developing Economies, Tokyo .
Reserve Bank of India (2002b), Report of the Working Group on Economic Indicators.
Jaya Mohanty, Bhupal Singh and Rajeev Jain (2003), Business Cycles and Leading
Indicators of Industrial Activity in India.
------------------------
Chitre presented evidence of synchronous movements in respect of a large number key economic processes including non-agricultural NNP, industrial production, capital formation, money stock, bank credit, etc. Chitre (1982) identified 15 indicators of growth cycles in India and constructed diffusion index and a composite index of these indicators and on the basis of these, characterized the Indian economy as having passed through five growth cycles in the overall economic activity during the period from 1951 to 1975.
Dua and Banerji (1999) have used the NBER approach to determine the dates of Indian business cycles and growth rate cycles and have reported six business cycle recessions in the Indian economy:
Business cycle recessions (India)
November 1964 to November 1965
April 1966 to April 1967
June 1972 to May 1973
April 1979 to March 1980
March 1991 to September 1991
May 1996 to February 1997
In a subsequent paper (2001), they have identified leading indicators and constructed a CLI index designed to anticipate business cycle and growth rate cycle upturns and downturns. However, the component series are not published in the above study.
Mall (1999) studied the cyclical behaviour of output variables such as real GDP,
non-agricultural GDP, GDP from manufacturing, trade; IIP, index of sales of private
corporate sector, etc. and has concluded that non-agricultural GDP can be taken as a
reference series for tracking business cycles in India. Using spectral analysis method, he has constructed a composite index of leading indicators to forecast cyclical movements in IIP from manufacturing sector.
A Working Group of the Reserve Bank of India (2002) on economic Indicators in
2001 examined the information base for the analysis of business cycles and explored the leading indicators approach for study of business cycles and forecasting. The working group seems to have taken the recommendation of Mall (1999) and used IIP as the reference series of non-agricultural GDP. Considering the IIP as the reference series, six series viz., Narrow money (M1), Non-food credit, WPI(raw materials), Production of coal and aluminium, and rail good traffic originated have been identified as leading indicators and the composite index has been constructed based on principal component analysis.
Chitre (2001) studied the business cycles in India for the period 1951-1982 and,
presented a selected list of leading, coincident and lagging indicators and the turning points and the diffusion index for the indices. Some of the
selected leading indicators are production of iron and aluminum, electricity generation, cheque clearances, etc.
Mohanty et al (2003) attempted dating of business cycles in India based on Bry-Boschan procedure and have identified 13 growth cycles of varying duration from 1970-71 to 2001-02. They have reported that average duration of recessions is higher at 16 months as compared to expansions with average duration of 12 months and the average of the cycles is 27 months.
http://www.oecd.org/dataoecd/54/48/34898082.pdf
Chitre, V. S.( 1982), " Growth Cycles in the Indian Economy ," Artha Vijnana, 24, 293- 450.
___ (1991), " Fluctuations in Agricultural Income , Public Sector Investment, World
Economic Activity and Business Cycles in India ", in H. Osada and D. Hiratsuka (eds.),
Business Cycles in asia , Institute of Developing Economies, Tokyo.
___(2001)," Indicators of Business Recessions and Revivals in India :1951-82", Indian
Economic Review , Vol. XXXVI, no. 1 ,2001.
Dua, P. and A. Banerji ( 1991 ) , '' An Index of Coincident Economic Indicators for the
Indian Economy", Journal of Quantitative Economics, Vol. 15, No. 2
____(2001) ,"An Indicator Approach to Business and Growth Rate Cycles : The case of
India ", Indian Economic Review, Vol. 36, No. 1 .
Hatekar, N. (1994), "Historical Behaviour of the Business Cycles in India: Some Stylized Facts for 1951-85", Journal of Indian School of Political Economy, Vol.6, No. 4.
Mall O.P. (1991),"Composite Index of Leading Indicators for Business Cycles in India ", RBI Occasional Papers , Vol 20, no. 3.
Nakamura J. (1991), "Fluctuations of Indian Economy, " in H. Osada and D. Hiratsuka
(eds.), Business Cycles in Asia , Institute of Developing Economies, Tokyo .
Reserve Bank of India (2002b), Report of the Working Group on Economic Indicators.
Jaya Mohanty, Bhupal Singh and Rajeev Jain (2003), Business Cycles and Leading
Indicators of Industrial Activity in India.
------------------------
Chitre presented evidence of synchronous movements in respect of a large number key economic processes including non-agricultural NNP, industrial production, capital formation, money stock, bank credit, etc. Chitre (1982) identified 15 indicators of growth cycles in India and constructed diffusion index and a composite index of these indicators and on the basis of these, characterized the Indian economy as having passed through five growth cycles in the overall economic activity during the period from 1951 to 1975.
Dua and Banerji (1999) have used the NBER approach to determine the dates of Indian business cycles and growth rate cycles and have reported six business cycle recessions in the Indian economy:
Business cycle recessions (India)
November 1964 to November 1965
April 1966 to April 1967
June 1972 to May 1973
April 1979 to March 1980
March 1991 to September 1991
May 1996 to February 1997
In a subsequent paper (2001), they have identified leading indicators and constructed a CLI index designed to anticipate business cycle and growth rate cycle upturns and downturns. However, the component series are not published in the above study.
Mall (1999) studied the cyclical behaviour of output variables such as real GDP,
non-agricultural GDP, GDP from manufacturing, trade; IIP, index of sales of private
corporate sector, etc. and has concluded that non-agricultural GDP can be taken as a
reference series for tracking business cycles in India. Using spectral analysis method, he has constructed a composite index of leading indicators to forecast cyclical movements in IIP from manufacturing sector.
A Working Group of the Reserve Bank of India (2002) on economic Indicators in
2001 examined the information base for the analysis of business cycles and explored the leading indicators approach for study of business cycles and forecasting. The working group seems to have taken the recommendation of Mall (1999) and used IIP as the reference series of non-agricultural GDP. Considering the IIP as the reference series, six series viz., Narrow money (M1), Non-food credit, WPI(raw materials), Production of coal and aluminium, and rail good traffic originated have been identified as leading indicators and the composite index has been constructed based on principal component analysis.
Chitre (2001) studied the business cycles in India for the period 1951-1982 and,
presented a selected list of leading, coincident and lagging indicators and the turning points and the diffusion index for the indices. Some of the
selected leading indicators are production of iron and aluminum, electricity generation, cheque clearances, etc.
Mohanty et al (2003) attempted dating of business cycles in India based on Bry-Boschan procedure and have identified 13 growth cycles of varying duration from 1970-71 to 2001-02. They have reported that average duration of recessions is higher at 16 months as compared to expansions with average duration of 12 months and the average of the cycles is 27 months.
Saturday, February 2, 2008
CFA Level 1 Blog
For CFA level1 Materials
http://www.nrao-cfal1.blogspot.com/
Study Session Alternative Investments
http://nrao-cfal1.blogspot.com/search/label/Alternative-investments
http://nrao-cfal1.blogspot.com/2008/02/cfa-level-1-alt-inv-venture-capital.html
http://www.nrao-cfal1.blogspot.com/
Study Session Alternative Investments
http://nrao-cfal1.blogspot.com/search/label/Alternative-investments
http://nrao-cfal1.blogspot.com/2008/02/cfa-level-1-alt-inv-venture-capital.html
Friday, February 1, 2008
Algorithmic Trading - A report
http://www.aitegroup.com/reports/200610311.php
A New Report from Aite Group
Algorithmic Trading 2006: More Bells and Whistles
Algorithmic trading has hit the mainstream in the U.S. equities market. For operational efficiency as well as a continued drive for capturing alpha, algorithmic trading is increasingly becoming the execution tool of choice for both the sell-side and the buy-side traders. Aite Group estimates that at the end of 2006, the share of algorithmic trading will approach 33% of the total equities trading volume. By the end of 2010, Aite Group estimates that approximately 53% of all equities trading will be done through algorithmic trading.
Algorithmic trading continues to grow, according to a new Aite Group report. The adoption of first-generation algorithms appears to be nearing its end in the U.S. market. Instead, most brokers have moved on to develop more sophisticated algorithms that are capable of supporting portfolio trading, adapting to real-time changing market conditions, and seeking darks pools of liquidity.
This report provides an update on the state of the algorithmic trading market and focuses on identifying new market trends and highlighting market challenges. The report also provides an estimated adoption rate for algorithmic trading services in the U.S. securities industry.
According to Brad Bailey, a Senior Analyst at Aite Group and co-author of the report, "The role of the buy-side trader has become very complicated. There are so many routes to getting trades done and finding liquidity; the landscape for trading is evolving quickly, as are algorithms."
Job Opportunities
The increasing use of algorithmic trading will benefit those with the right combination of IT skills, trading knowledge and expertise with quantitative trading strategies.
Especially in demand are those with quantitative research skills, as well as individuals who can bring their own trading strategies to hedge funds and bulge bracket firms, according to Lou Ricci, president of the Hagan-Ricci Group, a New York City firm specializing in front office hiring in IT, trading and quantitative research.
"On the lower end, the ideal job candidate in the algorithmic trading arena is the person with the right programming skills. At the top of the game are the Ph.D. types who can build the algorithms, as well as traders who can bring together the strategy and IT side of it," Ricci says.
Annual pay ranges from $250,000 to $1 million or more, he adds, depending on skill sets, job experience and rank.
Job opportunities are also increasing abroad, as firms in Europe and Asia adopt algorithmic trading models.Large brokers have already moved into major European markets and, to a lesser degree, Asian markets to provide their various automated trading strategies to the global buy-side clients.
http://news.efinancialcareers.co.uk/NEWS_ITEM/newsItemId-9145
Risks of Trading
Sept. 19, 2007
BOSTON (MarketWatch)
http://www.marketwatch.com/news/story/morgan-stanley-says-significant-losses/story.aspx?guid=%7B7A55FBE9%2DB5F6%2D4164%2D869A%2D93F3CCAA1054%7D&siteid=rss
Morgan Stanley said it saw "significant trading losses" in its quantitative strategies in the fiscal third quarter. The company said the losses partially offset record results in derivatives and prime brokerage and record trading volumes in its core equity business. Morgan Stanley said equity sales and trading net revenue rose 16% from a year earlier to $1.8 billion.
Economic Times article on 27 October 2007 (page 20) gives the figure of loss as $480 million from the trading desk that employed computer generated models to trade and make money.
A New Report from Aite Group
Algorithmic Trading 2006: More Bells and Whistles
Algorithmic trading has hit the mainstream in the U.S. equities market. For operational efficiency as well as a continued drive for capturing alpha, algorithmic trading is increasingly becoming the execution tool of choice for both the sell-side and the buy-side traders. Aite Group estimates that at the end of 2006, the share of algorithmic trading will approach 33% of the total equities trading volume. By the end of 2010, Aite Group estimates that approximately 53% of all equities trading will be done through algorithmic trading.
Algorithmic trading continues to grow, according to a new Aite Group report. The adoption of first-generation algorithms appears to be nearing its end in the U.S. market. Instead, most brokers have moved on to develop more sophisticated algorithms that are capable of supporting portfolio trading, adapting to real-time changing market conditions, and seeking darks pools of liquidity.
This report provides an update on the state of the algorithmic trading market and focuses on identifying new market trends and highlighting market challenges. The report also provides an estimated adoption rate for algorithmic trading services in the U.S. securities industry.
According to Brad Bailey, a Senior Analyst at Aite Group and co-author of the report, "The role of the buy-side trader has become very complicated. There are so many routes to getting trades done and finding liquidity; the landscape for trading is evolving quickly, as are algorithms."
Job Opportunities
The increasing use of algorithmic trading will benefit those with the right combination of IT skills, trading knowledge and expertise with quantitative trading strategies.
Especially in demand are those with quantitative research skills, as well as individuals who can bring their own trading strategies to hedge funds and bulge bracket firms, according to Lou Ricci, president of the Hagan-Ricci Group, a New York City firm specializing in front office hiring in IT, trading and quantitative research.
"On the lower end, the ideal job candidate in the algorithmic trading arena is the person with the right programming skills. At the top of the game are the Ph.D. types who can build the algorithms, as well as traders who can bring together the strategy and IT side of it," Ricci says.
Annual pay ranges from $250,000 to $1 million or more, he adds, depending on skill sets, job experience and rank.
Job opportunities are also increasing abroad, as firms in Europe and Asia adopt algorithmic trading models.Large brokers have already moved into major European markets and, to a lesser degree, Asian markets to provide their various automated trading strategies to the global buy-side clients.
http://news.efinancialcareers.co.uk/NEWS_ITEM/newsItemId-9145
Risks of Trading
Sept. 19, 2007
BOSTON (MarketWatch)
http://www.marketwatch.com/news/story/morgan-stanley-says-significant-losses/story.aspx?guid=%7B7A55FBE9%2DB5F6%2D4164%2D869A%2D93F3CCAA1054%7D&siteid=rss
Morgan Stanley said it saw "significant trading losses" in its quantitative strategies in the fiscal third quarter. The company said the losses partially offset record results in derivatives and prime brokerage and record trading volumes in its core equity business. Morgan Stanley said equity sales and trading net revenue rose 16% from a year earlier to $1.8 billion.
Economic Times article on 27 October 2007 (page 20) gives the figure of loss as $480 million from the trading desk that employed computer generated models to trade and make money.
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