کاربرد تئوری های علم بوم شناسی در علم مالی
الموضوعات : دانش سرمایهگذاریمحمد صالحی فر 1 , فریدون رهنمای رودپشتی 2 , حسن چهارمحالی 3
1 - دانشجوی دکتری مدیریت مالی دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران، ایران (نویسنده مسئول)
2 - استاد و عضو هیات علمی دانشگاه آزاد اسلامی واحد علوم و تحقیقات تهران، عضو موسس و دبیرکل انجمن مهندسی مالی ایران
3 - مدرس دانشگاه پدافند هوایی خاتم الانبیاء(ص)، تهران، ایران
الکلمات المفتاحية: مالی, بوم شناسی, جستجوی بهینه, انتخاب طبیعی, رفتار حیوانی,
ملخص المقالة :
در این مقاله به بررسی تئوری های بوم شناسی می پردازیم که در توضیح رفتارها در بازارهای مالی می توانند مورد استفاده قرار گیرند. اگر چه تا به حال رفتار توده واری برای توضیح بازارهای مالی مطرح شده است (بازارهای روبه رونق و روبه رکود، رفتار گله ای)، اما معتقدیم بسیاری از تئوری های موجود در حوزه بوم شناسی هنوز مورد مطالعه قرار نگرفته اند و تابه حال از آن ها چشم پوشی شده است. در این مقاله نشان می دهیم که پتانسیل قابل ملاحظه ای برای برقراری ارتباط بین تئوری های مطرح در بازارهای مالی و اصول علم بوم شناسی همچون تئوری جستجوگری بهینه، نظریه ارزش نهایی، آستانه اندازه شکار، شکار و خوراک جویی، مصون سازی شرط بندی، انتخاب طبیعی، رفتار حیوانی و رفتار آب وهوایی و فشارِگونه های غیربومی وجود دارد.
* Baddeley, M 2010, ‘Herding, social influence and economic decision-making: sociopsychological and neuroscientific analyses’, Philosophical Transactions of the Royal Society, vol. 365, pp. 281-290.
* Ball, S, Eckel, CC, Heracleous, M 2010, ‘Risk aversion and physical prowess: Prediction, choice and bias’, Journal of Risk and Uncertainty, vol. 41, no. 3, pp. 167-193.
* Bradie, J, Chivers, C, Leung, B, 2013, ‘Importing risk: quantifying the propagule pressure–establishment relationship at the pathway level’, Diversity and Distributions, vol. 19, pp. 1020-1030.
* Breuner, CW, Sprague, RS, Patterson, SH, Woods, HA, 2013, ‘Environment, behavior and physiology: do birds use barometric pressure to predict storms?’, Journal of Experimental Biology, vol. 216, pp. 1982-1990.
* Byun, C, de Blois, S, Brisson, J, 2013, ‘Plant functional group identity and diversity determine biotic resistance to invasion by an exotic grass’, Journal of Ecology, vol. 101, pp. 128–139.
* Caraco T, 1981, ‘Energy budgets, risk and foraging preferences in dark-eyed juncos (Junco hyemalis)’, Behavioral Ecology and Sociobiology, vol. 8, pp. 213-217.
* Caraco T, Chasin M 1984, ‘Foraging preferences: response to reward skew’, Animal Behavior, vol. 32, pp. 76-85.
* Caraco T, Kacelnik A, Mesnik N, Smulewitz M 1992, ‘Short-term rate maximization when rewards and delays covary’, Animal Behavior, vol. 44, pp. 441-447.
* Caraco T, Martindale S, Whittam TS 1980, ‘An empirical demonstration of risk sensitive foraging preferences’, Animal Behavior, vol. 28, pp. 820-830.
* Cesar, HSJ, van Beukering, PJH 2004, ‘Economic valuation of the coral reefs of Hawai’i’, Pacific Science, vol. 58, no. 2, pp. 231-242.
* Charnov, EL 1976, ‘Optimal Foraging: The Marginal Value Theorem’, Theoretical Population Biology, vol. 9, no. 2, pp. 129-136.
* Childress, MJ, Lung, MA 2003, ‘Predation risk, gender and the group size effect: does elk vigilance depend on the behaviour of conspecifics?’, Animal Behaviour, vol. 66, pp. 389-398.
* Childs, DZ, Metcalf, CJE, Rees, M, 2010, ‘Evolutionary bet-hedging in the real world: empirical evidence and challenges revealed by plants’, Proceedings of the Royal Society of London Series B, vol. 277, no. 1697, pp. 3055-3064.
* Clark, GF, Johnston, EL, 2009, ‘Propagule pressure and disturbance interact to overcome biotic resistance of marine invertebrate communities’, Oikos, vol. 118, pp. 1679-1686.
* Clerc, M. 2005, Particle swarm optimization, Hermes Science/Lavoisier Pub. 229-231.
* Coulatti, RI, 2005, ‘Are characteristics of introduced salmonid fishes biased by propagule pressure?’, Canadian Journal of Fisheries and Aquatic Sciences, vol. 62, no. 4, pp. 950-959.
* Crean, AJ, Marshall, DJ, 2009, ‘Coping with environmental uncertainty: dynamic bet hedging as a maternal effect’, Philosophical Transactions of the Royal Society B, vol. 364, pp. 1087-1096.
* Creel, S, Winnie, JA, Christianson, D, 2009, ‘Glucocorticoid stress hormones and the effect of predation risk on elk reproduction’, Proceedings of the National Academy of Sciences, vol. 106, pp. 12388–12393.
* de Groot, RS 1994, ‘Environmental functions and the economic value of natural ecosystems, In: Jansson, AM, Hammer, M, Folk, C, Costanza, R (eds), ‘Investing in Natural Capital: The Ecological Economics Approach to Sustainability’, Island Press, Washington, California.
* Delgado, MM, Nicholas, M, Petrie, DJ, Jacobs, LF, 2014, ‘Fox Squirrels Match Food Assessment and Cache Effort to Value and Scarcity’, PLoS ONE, vol. 9, no. 3, pp. 1- 8.
* Devenow, A, Welch, I 1996, ‘Rational herding in financial economics’, European Economic Review, vol. 40, pp. 603-615.
* Dicembrino, C, Scandizzo, PL, 2012, ‘Can Portfolio Diversification Increase Systemic Risk?:Evidence from the US and European Mutual Funds Market’, IUP Journal of Financial Risk Management, vol. 9, no. 4, pp. 52-76.
* Disatnik, D, Steinhart, Y, 2015, ‘Need for Cognitive Closure, Risk Aversion, Uncertainty Changes, and Their Effects on Investment Decisions’, Journal of Marketing Research, vol. 52, no. 3, pp. 349-359.
* Dziminski, MA, Vercoe, PE, Roberts, JD, 2008, ‘Variable offspring provisioning and fitness: a direct test in the field’, Functional Ecology, vol. 23, pp. 164-171.
* Elton, CS 1958, ‘The ecology of invasions by animals and plants’, Meuthen, London, UK.
* Gai, P. and Kapadia, S., 2010, Contagion in financial networks, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science, 466, 2401-2423.
* Gamelon, M, Gaillard, J, Baubet, E, Devillard, S, Say, L, Brandt, S, Gimenez, O, 2013, ‘The relationship between phenotypic variation among offspring and mother body mass in wild boar: evidence of coin-flipping?’, Journal of Animal Ecology, vol. 82, no. 5, pp. 937-945.
* Gaudiano, P, Shargel, B, Bonabeau, E, Clough, BT 2003, ‘Swarm intelligence: A new C2 paradigm with an application to control swarms of UAVs’, 8th ICCRTS Command and Control Research and Technology Symposium (2003).
* Gerard, J, Gerard, O, Bideau, E, Maublanc, M, Loisel, P, Marchal, C, 2002, ‘Herd Size in Large Herbivores: Encoded in the Individual or Emergent?’, Biological Bulletin, vol.202, pp. 275-282.
* Goetzmann, WN, Dasol, K, Alok, K, Wang, Q, 2015, ‘Weather-Induced Mood, Institutional Investors, and Stock Returns’, Review of Financial Studies, vol. 28, no. 1, pp. 73-111.
* Goetzmann, WN, Zhu, N, 2005, ‘Rain or Shine: Where is the Weather Effect?’, European Financial Management, vol. 11, no. 5, pp. 559–578.
* Golman, R, Hagmann, D, Miller, JH 2015, ‘Polya’s bees: A model of decentralized decision making, Science Advances, vol. 1, November, pp. 1-7.
* Heupel, MR, Simpfendorfer, CA, Hueter, RE, 2003, ‘Running before the storm: blacktip sharks respond to falling barometric pressure associated with Tropical Storm Gabrielle’, Journal of Fish Biology, vol. 63, pp. 1357–1363.
* Hight, GN, 2010, ‘Diversification Effect: Isolating the Effect of Correlation on Portfolio Risk’, Journal of Financial Planning, vol. 23, no. 5, pp. 54-61.
* Hiltunen, Ruuhela, R, Ostamo, A, Lönnqvist, J, Suominen, K, Partonen, T, 2012, ‘Atmospheric pressure and suicide attempts in Helsinki, Finland’, International Journal of Biometeorology, vol. 56, no. 6, pp. 1045-1053.
* Hirsh, AE, Gordon, DM 2001, ‘Distributed problem solving in social insects’, Annals of Mathematics and Artificial Intelligence, vol. 31, pp. 199-221.
* Hirshleifer, D, Shumway, T, 2003, ‘Good day sunshine: stock returns and the weather’, Journal of Finance, vol. 58, no. 3, pp. 1009-32.
* Hirshleifer, D, Shumway, T, 2003, ‘Good day sunshine: stock returns and the weather’, The Journal of Finance, vol. 58, no. 3, pp. 1009-1032.
* Ho, S, Li, A, Kinsun, T, Zhang, F 2015, ‘CEO gender, ethical leadership and accounting conservatism’, Journal of Business Ethics, vol. 127, no. 2, pp. 351-370.
* Hossain, M.A., Ferdous, I, 2015, ‘Autonomous robot path planning in dynamic environment using a new optimization technique inspired by bacterial foraging technique’, Robotics and Autonomous Systems, vol. 64, pp. 137-141.
* Jones, EI, Dornhaus, A, 2011, ‘Predation risk makes bees reject rewarding flowers and reduce foraging activity’, Behavioral Ecology and Sociobiology, vol. 65, no. 8, pp. 1505-1511.
* Kirkman, A 1993, ‘Ants, Rationality, and Recruitment’, The Quarterly Journal of Economics, vol. 108, no. 1, pp. 137-156.
* Koenig, S, Szymanski, B, Liu, Y 2001, ‘Efficient and inefficient ant coverage methods’, Annals of Mathematics and Artificial Intelligence, vol. 31, pp. 41-76.
* Koprivnikar, J, Penalva, L, 2015, ‘Lesser of Two Evils? Foraging Choices in Response to Threats of Predation and Parasitism’, PLoS One, vol. 10, no. pp. 1-11.
* Kraus, B, 1983, ‘A test of the optimal density model for seed scatter-hoarding’, Ecology, vol. 64, pp. 608–610.
* Lee, PC 1987, ‘Allomothering among African elephants’, Animal behaviour, vol. 35, pp. 278-291.
* Lima, SL, Dill, LM 1990, ‘Behavioral decisions made under the risk of predation: a review and prospectus’, Canadian Journal of Zoology, vol. 68, no. 4, pp. 619-640.
* Linnainmaa, JT, 2011, ‘Why do some households trade so much?’ Review of Financial Studies vol. 24 (5), pp. 1630–1666.
* Liu, S, Costanzo, R, Troy, A, D’Aagostino, J, Mates, W 2010, ‘Valuing New Jersey’s ecosystem services and natural capital’, Environmental Management, vol. 45, pp. 1271–1285.
* Locke, PR, Mann SC 2015 ‘Learning by aspiring professional traders: Learning to take risk.’ Journal of Behavioral and Experimental Finance, vol. 8, pp. 54-63.
* Mahani, R, Bernhardt, D 2007 ‘Financial speculators’ underperformance: Learning, selfselection, and endogenous liquidity.’ Journal of Finance, vol. 62, pp. 1313–1340.
* Marshall, HH, Carter, AJ, Ashford, A, Rowcliffe, JM, Cowlinshaw, G 2013, ‘How do foragers decide when to leave a patch? A test of alternative models under natural and experimental conditions’, Journal of Animal Ecology, vol. 82, pp. 894–902.
* May RM, Levin SA and Sugihara G, 2008, Ecology for bankers. Nature, 451 (7181), 893- 895.
* May, R.M., and Arinaminpathy, N., 2010, Systemic risk: the dynamics of model banking systems, Interface, 7, 823-838.
* May, RM, Levin, SA, Sugihara, G 2008, ‘Ecology for bankers’, Nature, vol. 451, no. 21, pp. 893-895.
* Messis, P, Zapranis, A 2014, ‘Herding towards higher moment CAPM, contagion of herding and macroeconomic shocks: Evidence from five major developed markets’, Journal of Behavioral and Experimental Finance, vol. 4, no. 1 pp. 1-13.
* Nguyen, Y, Noussair, CN 2014, ‘Risk aversion and emotions’, Economic Review, vol. 19, no. 3, pp. 296-312.
* Penner, JL, Zalocusky, K, Holifield, L, Abernathy, J, McGuff, B, Schichtl, S, Weaver, W, Moran, MD, 2013, ‘Are High Pilferage Rates Influenced by Experimental Design? The Effects of Food Provisioning on Foraging Behavior’, Southeastern Naturalist, vol. 12, no. 3, pp. 589-598.
* Reichman, OJ, Jones, MB, Schildhauer, MP 2011, ‘Challenges and opportunities of open data in ecology’, Science, vol. 331, no. 6018, pp. 702-705.
* Ross, SA, Westerfield, RW, Jaffe, JF, 2012, Corporate Finance, 10th edn, McGraw-Hill/Irwin, New York, NY.
* Rychlik, L, Jancewicz, E 2002, ‘Prey size, prey nutrition, and food handling by shrews of different body sizes’, Behavioral Ecology, vol. 13, no. 2, pp. 216-223.
* Sagata, K, Lester, P, 2009, ‘Behavioural plasticity assoiciated with propagule size, resources, and the invasion success of the Argentine ant Linepithema humile’, Journal of Applied Ecology, vol. 46, no. 1, pp. 19-27.
* Samelius, G, Alisauskas, RT, Larivière, S, 2007, ‘Survival rate of experimental food caches: implications for arctic foxes’, Canadian Journal of Zoology, vol. 85, no. 3, pp. 397-403.
* Saunders, EM, 1993, ‘Stock prices and Wall Street weather’, American Economic Review, vol. 83, pp. 1337-45.
* Simberloff, D 2009, ‘The role of propagule pressure in biological invasions’ Annual Review of Ecology, Evolution and Systematics, vol. 40, pp. 81-102.
* Sprout JC 2004, ‘Competition with evolution in ecology and finance’, Physics Letters, vol. 325, pp. 329-333.
* Stephens, DW, Paton, SR, 1986, ‘How constant is the constant of risk-aversion?’, Animal Behavior, vol. 34, pp. 1659-1667.
* Tambling, C, J, Druce, DJ, Hayward, DJ, Hayward, MW, Castley, JG, Adendorff, J, Kerley, GIH, 2012, ‘Spatial and temporal changes in group dynamics and range use enable anti-predator responses in African buffalo’, Ecology, vol. 93, no. 6, pp. 1297-1304.
* Viscido, SV, 2003, ‘The Case for the Selfish Herd Hypothesis’, Comments on Theoretical Biology, vol. 8, pp. 665–684.
* Volterra, V., 1931, Variations and fluctuations of the number of individuals in animal species living together in Animal Ecology, Chapman, R.N. (ed), McGraw–Hill.
* Von Holle, B, Simberloff, D, 2005, ‘Ecological resistance to biological invasion overwhelmed by propagule pressure’, Ecology, vol. 86, no. 12, pp. 3212-3218.
* Wagner, IA, Bruckstein, AM 2001, ‘From Ants to A(ge)nts: A special issue on Ant- Robotics’, Annals of Mathematics and Artificial Intelligence, vol. 31, pp. 1-5.
* Wang, Y, Zhou, W, Chang, K 2013, ‘Effect of decision makers’ education levels on their corporate risk taking’, Social Behavior and Personality: An International Journal, vol. 41, no. 7, pp. 1225-1230.
* Western D, Lindsay, WK 1984, ‘Seasonal herd dynamics of a savannah elephant populations’, African Journal of Ecology, vol. 22, no. 4, pp. 229-244.
* Wittmann, MJ, Metzler, D, Gabriel, W, Jeschke, JM, 2014, ‘Decomposing propagule pressure: the effects of propagule size and propagule frequency on invasion success’, Oikos, vol. 123, pp. 441–450.
* Wood, A, Ackland, GJ, 2007, ‘Evolving the selfish herd: emergence of distinct aggregating strategies in an individual-based model’, Proceedings B: Biological Sciences, vol. 274, no. 1618, pp. 1637-1642.
_||_